118 lines
3.8 KiB
Markdown
118 lines
3.8 KiB
Markdown
---
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layout: default
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title: Reranking search results
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parent: Search relevance
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has_children: false
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nav_order: 60
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---
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# Reranking search results
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Introduced 2.12
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{: .label .label-purple }
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You can rerank search results using a cross-encoder reranker in order to improve search relevance. To implement reranking, you need to configure a [search pipeline]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/index/) that runs at search time. The search pipeline intercepts search results and applies the [`rerank` processor]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/rerank-processor/) to them. The `rerank` processor evaluates the search results and sorts them based on the new scores provided by the cross-encoder model.
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**PREREQUISITE**<br>
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Before configuring a reranking pipeline, you must set up a cross-encoder model. For more information, see [Cross-encoder models]({{site.url}}{{site.baseurl}}/ml-commons-plugin/custom-local-models/#cross-encoder-models).
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{: .note}
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## Running a search with reranking
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To run a search with reranking, follow these steps:
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1. [Configure a search pipeline](#step-1-configure-a-search-pipeline).
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1. [Create an index for ingestion](#step-2-create-an-index-for-ingestion).
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1. [Ingest documents into the index](#step-3-ingest-documents-into-the-index).
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1. [Search using reranking](#step-4-search-using-reranking).
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## Step 1: Configure a search pipeline
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Next, configure a search pipeline with a [`rerank` processor]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/rerank-processor/).
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The following example request creates a search pipeline with an `ml_opensearch` rerank processor. In the request, provide a model ID for the cross-encoder model and the document fields to use as context:
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```json
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PUT /_search/pipeline/my_pipeline
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{
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"description": "Pipeline for reranking with a cross-encoder",
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"response_processors": [
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{
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"rerank": {
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"ml_opensearch": {
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"model_id": "gnDIbI0BfUsSoeNT_jAw"
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},
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"context": {
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"document_fields": [
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"passage_text"
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]
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}
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}
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}
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]
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}
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```
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{% include copy-curl.html %}
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For more information about the request fields, see [Request fields]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/rerank-processor/#request-fields).
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## Step 2: Create an index for ingestion
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In order to use the rerank processor defined in your pipeline, create an OpenSearch index and add the pipeline created in the previous step as the default pipeline:
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```json
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PUT /my-index
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{
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"settings": {
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"index.search.default_pipeline" : "my_pipeline"
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},
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"mappings": {
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"properties": {
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"passage_text": {
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"type": "text"
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}
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}
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}
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}
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```
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{% include copy-curl.html %}
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## Step 3: Ingest documents into the index
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To ingest documents into the index created in the previous step, send the following bulk request:
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```json
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POST /_bulk
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{ "index": { "_index": "my-index" } }
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{ "passage_text" : "I said welcome to them and we entered the house" }
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{ "index": { "_index": "my-index" } }
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{ "passage_text" : "I feel welcomed in their family" }
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{ "index": { "_index": "my-index" } }
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{ "passage_text" : "Welcoming gifts are great" }
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```
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{% include copy-curl.html %}
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## Step 4: Search using reranking
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To perform reranking search on your index, use any OpenSearch query and provide an additional `ext.rerank` field:
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```json
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POST /my-index/_search
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{
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"query": {
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"match": {
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"passage_text": "how to welcome in family"
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}
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},
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"ext": {
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"rerank": {
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"query_context": {
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"query_text": "how to welcome in family"
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}
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}
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}
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}
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```
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{% include copy-curl.html %}
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Alternatively, you can provide the full path to the field containing the context. For more information, see [Rerank processor example]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/rerank-processor/#example). |