Skip to main content
A Similarity search query retrieves an extensive list of potentially relevant results from hundreds of millions of documents. Documents are matched and ranked to provide the best possible results quickly. However, some of the results might not be as relevant as expected. To further improve the quality of results, it is possible to apply a second phase, a re-ranker based on a Cross-Encoder, to rank the most promising first-phase candidates again using the same text provided in the Similarity query. The re-ranker defines a new relevance value between 0 and 1 for each text chunk and drops those with a relevance value below the specified rerank_threshold provided. We recommend specifying a date_range and retrieving many documents or chunks so that all the first-phase chunks pass through the re-ranker. Only the returned chunks after the second phase will count as API query unit usage. The following request returns chunks from the previous week with a reranker relevance score of at least 0.9:
Higher thresholds return fewer, more precise results. The reranker uses query.text by default; set ranking_params.reranker.text when you want it to rank against different text. Why don’t we use the re-ranker in the first phase directly? The re-ranker model is optimized to rerank only a subset of the best candidates from the first phase, but it is not built to search millions of documents.