Semantic Search Without Embeddings (softwaredoug.com)

Recent discussions in the AI/ML community have highlighted a novel approach to semantic search that moves beyond traditional vector-based embeddings. This evolution is significant as it emphasizes the importance of mapping queries and content through shared hierarchical representations rather than relying solely on embeddings. The article illustrates this with practical examples, such as searching for items like "round red fruit" and shows how queries can be structured to improve search relevance by employing a taxonomy framework. By utilizing detailed hierarchies and a new hierarchical tokenizer in a standard BM25 index, it ensures that specificity in search terms is rewarded, potentially enhancing the accuracy of search results. The implications of this approach are profound for organizations using AI in their search functions. By combining traditional taxonomy management with modern LLM capabilities, the method allows for better categorization and retrieval of information that resonates more closely with user intent. Such a system not only provides a more structured method to gauge similarity but also ensures that users receive content that accurately matches their searches. This integration can significantly streamline the user experience in technical domains where precise information retrieval is critical, ultimately helping organizations leverage existing knowledge frameworks more effectively.