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Rerank processor
The rerank
search request processor intercepts search results and passes them to a cross-encoder model to be reranked. The model reranks the results, taking into account the scoring context. Then the processor orders documents in the search results based on their new scores.
Request fields
The following table lists all available request fields.
Field | Data type | Description |
---|---|---|
<reranker_type> | Object | The reranker type provides the rerank processor with static information needed across all reranking calls. Required. |
context | Object | Provides the rerank processor with information necessary for generating reranking context at query time. |
tag | String | The processor’s identifier. Optional. |
description | String | A description of the processor. Optional. |
ignore_failure | Boolean | If true , OpenSearch ignores any failure of this processor and continues to run the remaining processors in the search pipeline. Optional. Default is false . |
The ml_opensearch
reranker type
The ml_opensearch
reranker type is designed to work with the cross-encoder model provided by OpenSearch. For this reranker type, specify the following fields.
Field | Data type | Description |
---|---|---|
ml_opensearch | Object | Provides the rerank processor with model information. Required. |
ml_opensearch.model_id | String | The model ID for the cross-encoder model. Required. For more information, see Using ML models. |
context.document_fields | Array | An array of document fields that specifies the fields from which to retrieve context for the cross-encoder model. Required. |
Example
The following example demonstrates using a search pipeline with a rerank
processor.
Creating a search pipeline
The following request creates a search pipeline with a rerank
response processor:
PUT /_search/pipeline/rerank_pipeline
{
"response_processors": [
{
"rerank": {
"ml_opensearch": {
"model_id": "gnDIbI0BfUsSoeNT_jAw"
},
"context": {
"document_fields": [ "title", "text_representation"]
}
}
}
]
}
Using a search pipeline
Combine an OpenSearch query with an ext
object that contains the query context for the large language model (LLM). Provide the query_text
that will be used to rerank the results:
POST /_search?search_pipeline=rerank_pipeline
{
"query": {
"match": {
"text_representation": "Where is Albuquerque?"
}
},
"ext": {
"rerank": {
"query_context": {
"query_text": "Where is Albuquerque?"
}
}
}
}
Instead of specifying query_text
, you can provide a full path to the field containing text to use for reranking. For example, if you specify a subfield query
in the text_representation
object, specify its path in the query_text_path
parameter:
POST /_search?search_pipeline=rerank_pipeline
{
"query": {
"match": {
"text_representation": {
"query": "Where is Albuquerque?"
}
}
},
"ext": {
"rerank": {
"query_context": {
"query_text_path": "query.match.text_representation.query"
}
}
}
}
The query_context
object contains the following fields.
Field name | Description |
---|---|
query_text | The natural language text of the question that you want to use to rerank the search results. Either query_text or query_text_path (not both) is required. |
query_text_path | The full JSON path to the text of the question that you want to use to rerank the search results. Either query_text or query_text_path (not both) is required. The maximum number of characters in the path is 1000 . |
For more information about setting up reranking, see Reranking search results.