Hybrid Search with TimescaleDB: Vector, Keyword, and Temporal Filtering

The text explores the complexities of search methods, focusing on how vector, text, and hybrid searches perform differently in retrieving relevant documentation. While vector embeddings excel in capturing semantic meaning, they often miss exact terms and technical details, whereas text searches can handle specific keywords but struggle with synonyms and context. Hybrid search, which combines semantic and keyword results using Reciprocal Rank Fusion (RRF), is designed to overcome these limitations but can fail when both methods agree on outdated information. Temporal filtering emerges as a crucial component in maintaining the recency and relevance of search results by restricting the search to recent documents, thereby preventing outdated documentation from skewing results. The text further discusses the importance of schema design, index creation, and query construction in implementing an effective hybrid search system and provides a demonstration using TimescaleDB, showcasing the strengths and weaknesses of each search method and emphasizing the need for temporal awareness in certain use cases.