How are embeddings used in hybrid search systems?

Embeddings are used in hybrid search systems to enable semantic understanding of data while combining the strengths of keyword-based and vector-based search methods. In a hybrid system, embeddings transform text, images, or other data into numerical vectors that capture their meaning and relationships. These vectors allow the system to find matches based on conceptual similarity, even when exact keyword overlaps are missing. For example, a search for “cold dessert” could return results containing “ice cream” or “sorbet” because their embeddings are semantically close, even if those terms aren’t explicitly mentioned. This complements traditional keyword search, which relies on exact term matches or synonyms.

Hybrid systems merge results from both keyword and vector search to improve relevance. Keyword search excels at matching specific terms, filtering results quickly using inverted indexes. Meanwhile, embeddings handle ambiguous queries or cases where users describe concepts without using precise terminology. For instance, a developer searching for “Python error handling” might get keyword matches for tutorials containing those exact terms, while vector search surfaces articles about “exceptions” or “try-catch blocks.” The system then combines these results using techniques like score fusion—weighting and normalizing scores from both methods—to produce a unified ranked list. This approach ensures high precision from keywords and broader recall from embeddings.

Implementing embeddings in hybrid systems requires tools for generating vectors (e.g., BERT, Sentence Transformers) and databases that support combined search, like Elasticsearch with a vector plugin or Pinecone. Developers often precompute embeddings for indexed data and store them alongside keywords. At query time, the system runs parallel searches: one using keywords and another using the query’s embedding. Challenges include balancing latency (vector search can be slower) and tuning fusion algorithms to avoid bias toward one method. For example, an e-commerce app might use keyword search to filter products by brand names and embeddings to recommend visually similar items, ensuring users get precise and contextually relevant results.