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Version: 2.0

Maximal Marginal Relevance (MMR) Reranker

The Maximal Marginal Relevance (MMR) reranker enables you to diversify search results to reduce redundancy while maintaining relevance to the query. Search queries often result in a collection of similar documents that, while relevant, may lack variety. MMR addresses this by reranking the results to include documents that are both relevant to your query but also different from the documents already listed in the search results.

This approach provides users with a more balanced set of results as they may show different perspectives related to your query.

Enable the MMR reranker​

You enable the MMR reranker by specifying the type as mmr. Having a diverse set of relevant results has different benefits depending on the use case:

  • In a pure search scenario, it improves user engagement with results by avoiding repetition.
  • In a generative AI scenario, it produces more comprehensive summaries.
  • Diversifying results can potentially represent all points of view in the data or reduce bias.

In addition to specifying the type as mmr at query time, you also specify a diversity bias range between 0.0 and 1.0. Values closer to 1.0 optimize for the most diverse results. This setting is only available with the MMR Reranker.

"reranker": {
   "type": "mmr",
   "diversity_bias": 0.4
 },

To enable the Maximal Marginal Relevance Reranker in the Vectara Console UI:

  1. Open a corpus from the list and select the Query tab.

  2. Click Retrieval and a navigation drawer opens.

  3. Enable the Rerank search results option.

    Diversity Reranker

  4. Enter a value between 0.0 and 1.0 in the Diversity factor field.

  5. Close the Configure retrieval drawer and click Reload results.

By applying the MMR Reranker to queries, users get results that are not just relevant but diverse and comprehensive.