Similarity search in embeddings is a technique used to find data points that are mathematically “close” to a given query within a high-dimensional vector space. Embeddings represent objects—like text, images, or user profiles—as numerical vectors, capturing their semantic or structural features. Similarity search measures the distance between these vectors (using metrics like cosine similarity or Euclidean distance) to identify items that share characteristics with the query. This process is foundational for applications like recommendation systems, search engines, or anomaly detection, where identifying related items efficiently is critical.
For example, in a text-based search system, a query like “machine learning” might be converted into an embedding vector. The system then scans a database of precomputed document embeddings to find those with vectors closest to the query vector. Similarly, in e-commerce, product embeddings can help recommend items similar to a user’s past purchases by comparing their vector representations. Without similarity search, these tasks would require manually defining rules or features, which is impractical at scale. Embeddings abstract away the complexity, and similarity search provides a scalable way to operationalize that abstraction.
The core challenge lies in balancing speed and accuracy. Exact nearest-neighbor searches (like brute-force comparisons) are accurate but computationally expensive for large datasets. Approximate methods, such as Facebook’s FAISS or Spotify’s Annoy, use techniques like tree-based partitioning or quantization to group vectors into clusters, enabling faster but slightly less precise results. For instance, FAISS might index millions of vectors into buckets, allowing it to skip irrelevant comparisons during a search. Developers often choose tools based on their specific needs: exact methods for smaller datasets where precision is critical, and approximate approaches for real-time applications with large-scale data.