Embeddings are a crucial technique used in document retrieval systems to represent text data in a way that makes it easier to find relevant documents based on search queries. Essentially, embeddings convert words, sentences, or entire documents into numerical vectors in a high-dimensional space. This transformation allows for better comparisons between different pieces of text, as similar texts will have embeddings that are closer together in this space. When a user submits a search query, the system converts the query into an embedding. It can then calculate the similarity between this query embedding and the embeddings of the documents in its database to identify the most relevant results.
One common approach to document retrieval using embeddings is through cosine similarity. Once the embeddings for the query and the documents are created, the system computes the cosine similarity between the query embedding and each document's embedding. This similarity score reflects how closely related the query is to each document. For example, if a user searches for "best programming languages," the system can rank documents that contain relevant discussions or lists of programming languages by examining how closely aligned their embeddings are with the embedding of the query.
Moreover, embeddings facilitate more advanced search features, such as semantic search. This means the retrieval system can recognize synonymous phrases or related concepts even if they don’t contain exact keywords from the query. For instance, if a user searches for "data analysis tools," the system might retrieve documents talking about "statistics software" or "data visualization applications." Such flexibility improves the user experience by delivering more relevant information based on the underlying meaning of the terms rather than relying solely on keyword matching. This makes embeddings a powerful tool for creating efficient and user-friendly document retrieval systems.