ハイブリッド検索とは？

Hybrid search combines multiple search algorithms, enhancing precision and relevance by integrating keyword and vector searches, which leverage the strengths of both methods to provide users with an effective search experience. Weaviate, a vector database, utilizes both sparse and dense vectors, with the former requiring tokenizers like the newly introduced KAGOME_JA for Japanese text in Weaviate v1.28. This approach allows for the integration of the benefits of keyword searches, which excel in precise matches, and vector searches, which understand query context, creating an ideal search system for scenarios needing both capabilities. The current implementation in Weaviate uses algorithms like BM25/BM25F for sparse embeddings, while dense embeddings are generated from machine learning models like GloVe or Transformers. Hybrid search, exemplified by queries such as "spring dress," effectively combines vector representations for context (e.g., interpreting "spring" as floral patterns) with keyword precision for terms like "dress." By adjusting the alpha parameter, users can balance the weight between keyword and vector search results, offering flexibility in search systems. This functionality is further enhanced by the ability to use different tokenizers for Japanese text, providing nuanced search capabilities in Weaviate's hybrid search framework.