How are embeddings applied in search engines?

Embeddings are used in search engines to convert text, images, or other data into numerical vectors, enabling algorithms to understand semantic relationships between items. When a user enters a query, the search engine generates an embedding for that query and compares it to embeddings of indexed documents or products. This comparison is typically done using similarity metrics like cosine similarity, which measures how closely aligned two vectors are. For example, a query for “best budget laptops” might return results related to “affordable notebooks” even if the exact keywords don’t match, because their embeddings are semantically close. This approach moves beyond literal keyword matching to capture the intent and context of the search.

To implement this, search engines often use pre-trained models like BERT, Word2Vec, or sentence transformers to generate embeddings. These models are trained on large text corpora to map words, phrases, or entire documents into dense vector spaces. For instance, a product search engine might embed product descriptions and user queries into the same vector space, allowing it to retrieve items that align with the query’s meaning rather than just keyword overlaps. Vector databases like FAISS or Annoy are then used to efficiently search through millions of embeddings by approximating nearest neighbors. This reduces computational overhead compared to brute-force comparisons, making real-time semantic search feasible at scale.

A practical example is e-commerce platforms using embeddings to improve search results. If a user searches for “durable running shoes,” embeddings help the engine surface products labeled as “long-lasting trail sneakers” even if “durable” isn’t explicitly mentioned. Another use case is multilingual search, where embeddings trained on multiple languages map similar phrases across languages into the same vector space, enabling queries in one language to return results in another. Challenges include maintaining embedding quality as data evolves and balancing latency with accuracy in large-scale systems. Developers must also handle updates—for example, retraining embeddings when new products or content are added—to ensure relevance over time.