Reranking

Reranking search results involves a process of rescoring and refining an initial set of query results to achieve a more precise ranking. It employs a machine learning model that while slower than the rapid retrieval step, offers more accurate results. We currently support two rerankers: the Maximal Marginal Relevance (MMR) Reranker and the new Scale-only Multilingual Reranker v1.

Enable reranking

To enable reranking, specify the appropriate value for the rerankerId. The MMR reranker ID is 272725718 and the Multilingual Reranker v1 ID is 272725719. In most scenarios, it makes sense to use the default query start value of 0 so that you're reranking all of the best initial results. You can also set numResults of the query to the total number of documents you wish to rerank. The default value is 25.

The following example shows the numResults and rerankerId values in a query. Note that this simplified example intentionally omits
several parameter values.

{
  "query": "What is my question?",
  "stream_response": false,
    "search": {
    "start": 0,
    "limit": 25,
    "context_configuration": {},
    },
    "reranker": {
          "type": "customer_reranker",
          "reranker_id": "272725719"
    },
  "generation": [],
  "enable_factual_consistency_score": true
}

You can also enable reranking in the Vectara console after navigating to the Query tab of a corpus and selecting Retrieval.

note

Scale users have a drop-down menu to select different rerankers.

Vectara Multilingual Reranker v1

The new Vectara Multilingual Reranker V1 is a state-of-the-art reranking model that significantly enhances the precision of retrieved results across 100+ languages. To use this reranker, set the rerankerID as 272725719.

"reranker": {
      "type": "customer_reranker",
      "reranker_id": "272725719"
    }

The Vectara Multilingual Reranker ensures impressive zero-shot performance on unseen data and domains, and it never trains on customer data. In RAG use cases, this reranker distinguishes the scores of relevant and irrelevant documents in a query-independent manner. For more details about our Multilingual Reranker v1, check out these feature announcement and technical deep dive blogs.

Based on our experimentation we suggest using a cut-off threshold of 0.5 as a good starting point. This threshold value is the relevance score returned by Vectara with each responseAny results that achieve a score of greater than or equal to 0.5 can be considered relevant and anything below that can be considered as non-relevant.

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.

You enable the MMR reranker by specifying the reranker_id as 272725718. 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 rerankerId as 272725718 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": "customer_reranker",
   "reranker_id": "272725718"
   "mmrConfig": {
      "diversityBias": 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.

   

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.