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Version: 2.0

Rerank Search Results

Initial search results often fail to capture nuanced relevance or diversity, potentially leading to suboptimal user experiences. Utilizing Vectara's reranking can significantly enhance the quality and usefulness of search results, leading to more effective information retrieval. 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 provides the following rerankers:

Enable reranking

To enable reranking, specify the appropriate value for the type in the reranker object. For the MMR reranker, use mmr. 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 limit of the query to the total number of documents you wish to rerank. The default value is 25.

The following example shows the limit and type 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": "mmr",
"diversity_bias": "0.4"
},
"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.