# Creating a pipeline in Amazon OpenSearch Service
<a name="managed-opensearch-plugin-pipeline-example"></a>

After you [install the Amazon Personalize Search Ranking plugin](open-search-install-managed.md), you're ready to configure it by creating an OpenSearch search pipeline. 

A *search pipeline* is a set of request and response processors that run sequentially in the order that you create them. When you create a search pipeline for the plugin, you specify a `personalized_search_ranking` response processor. For information about search pipelines, see [Search pipelines](https://opensearch.org/docs/latest/search-plugins/search-pipelines/index/). 

After you create a search pipeline with a `personalized_search_ranking` response processor, you're ready to start applying the plugin to OpenSearch queries. You can apply it to an OpenSearch index or an individual OpenSearch query. For more information, see [Applying the plugin](managed-apply-plugin.md). 

You can use the following Python code to create a search pipeline with a `personalized_search_ranking` response processor on an OpenSearch Service domain. Replace `domain endpoint` with your domain endpoint URL. For example: `https://<domain name>.<Amazon region>.es-staging.amazonaws.com`. For a complete explanation of each `personalized_search_ranking` parameter, see [Fields for the `personalized_search_ranking` response processor](opensearch-plugin-pipeline-fields.md). 

```
import requests
from requests_auth_aws_sigv4 import AWSSigV4

domain_endpoint = 'domain endpoint'
pipeline_name = 'pipeline name'
url = f'{domain_endpoint}/_search/pipeline/{pipeline_name}'
auth = AWSSigV4('es')

headers = {'Content-Type': 'application/json'}

body = {
  "description": "A pipeline to apply custom re-ranking from Amazon Personalize",
  "response_processors": [
    {
      "personalized_search_ranking" : {
        "campaign_arn" : "Amazon Personalize Campaign ARN",
        "item_id_field" : "productId",
        "recipe" : "aws-personalized-ranking-v2",
        "weight" : "0.3",
        "tag" : "personalize-processor",
        "iam_role_arn": "Role ARN",
        "aws_region": "Amazon region",
        "ignore_failure": true
    }
  ]
}
try:
    response = requests.put(url, auth=auth, json=body, headers=headers, verify=False)
    print(response.text)
except Exception as e:
    print(f"Error: {e}")
```