In search engines, embeddings are used to improve the relevance and accuracy of search results by representing queries and documents as vectors in a shared embedding space. When a user submits a search query, the search engine converts the query into an embedding and compares it to embeddings of indexed documents or web pages. This allows the system to return documents that are semantically similar to the query, even if they do not contain the exact search terms.
Embeddings enable search engines to go beyond keyword matching and understand the meaning behind the query. For example, a query like “best Italian restaurants in New York” could return results for “top Italian eateries in NYC,” even though the exact phrasing doesn’t match. Search engines use embeddings generated by pre-trained models like BERT or GPT to understand the intent behind the search and retrieve the most relevant documents.
Additionally, embeddings are used in features like semantic search, where the search engine not only considers the query terms but also understands their context. This improves the quality of search results, especially in scenarios with complex or ambiguous queries. Embeddings allow search engines to rank documents based on relevance and semantic meaning, leading to more accurate and user-friendly search experiences.