Embeddings improve semantic search by representing words, phrases, or entire documents as numerical vectors in a high-dimensional space. This representation captures the contextual meaning and relationships between different pieces of information. Instead of relying solely on keyword matching, which can often miss nuances in language, embeddings allow the search system to understand synonyms and related terms. For instance, a search for "car" can return results for "automobile," "vehicle," or even "sedan" because these terms are geometrically close to each other in the embedding space.
One practical way embeddings enhance semantic search can be seen in document retrieval systems. For example, if a user queries "best practices in web development," a traditional search engine might struggle to pinpoint relevant articles that don’t explicitly use those exact words. However, systems using embeddings can identify documents that discuss related concepts like "frontend frameworks" or "website optimization" by recognizing the semantic similarity instead of exact word matches. This leads to more relevant search results and a better user experience.
Furthermore, embeddings support the incorporation of user intent in search results. When a user types a query, the system can analyze the vector representation of the query against the vector representations of available documents. This allows the system to rank results not just on keyword frequency but also on how well documents align with the underlying intent of the user's question. For example, searching for "how to grow tomatoes" can bring up articles that include practical gardening tips, solving common tomato-growing problems, or even related recipes, thereby providing a broader and more useful context tailored to what the user is actually looking for.