Embeddings are a crucial component in hybrid search systems, which combine traditional keyword-based search with semantic search capabilities. In a hybrid system, embeddings represent data, such as documents and queries, in a mathematical format that captures their meanings. This allows the system to understand both the exact words used and the underlying concepts, resulting in more relevant search results. By using embeddings, the system can match queries with documents that might not share common keywords but are thematically related.
For example, consider a search engine for academic articles. A user may input a query like "climate change mitigation strategies." A traditional keyword search might return articles that contain exactly those words. However, by utilizing embeddings, the hybrid system can also find articles that discuss related topics, such as "reducing carbon emissions" or "sustainable agricultural practices." The embeddings allow the system to gauge the semantic similarity between the query and a broader set of documents, enhancing the search experience.
Furthermore, hybrid search systems can balance efficiency and accuracy by integrating embeddings with traditional information retrieval techniques. When a user performs a search, the system can first filter through a larger corpus using established keyword matching methods to narrow down the results. Then, it applies embeddings to refine this smaller set, retrieving documents that align more closely with the user’s intent. This two-step process not only improves the relevance of results but also maintains performance efficiency, making it practical for real-world applications in various domains.