What is DRIFT search

 However, we haven’t yet explored DRIFT search, which will be the focus of this blog post. DRIFT is a newer approach that combines characteristics of both global and local search methods. The technique begins by leveraging community information through vector search to establish a broad starting point for queries, then uses these community insights to refine the original question into detailed follow-up queries. This allows DRIFT to dynamically traverse the knowledge graph to retrieve specific information about entities, relationships, and other localized details, balancing computational efficiency with comprehensive answer quality

DRIFT search presents an interesting strategy for balancing the breadth of global search with the precision of local search. By starting with community-level context and progressively drilling down through iterative follow-up queries, it avoids the computational overhead of processing all community reports while still maintaining comprehensive coverage.

However, there’s room for several improvements. The current implementation treats all intermediate answers equally, but filtering based on their confidence scores could improve final answer quality and reduce noise. Similarly, follow-up queries could be ranked by relevance or potential information gain before execution, ensuring the most promising leads are pursued first.

Another promising enhancement would be introducing a query refinement step that uses an LLM to analyze all generated follow-up queries, grouping similar ones to avoid redundant searches and filtering out queries unlikely to yield useful information. This could significantly reduce the number of local searches while maintaining answer quality.

https://towardsdatascience.com/implementing-drift-search-with-neo4j-and-llamaindex/

https://towardsdatascience.com/implementing-drift-search-with-neo4j-and-llamaindex/