Opnesearch Learning-to-Rank POC

This repo is intended as for educational purposes. It contains the steps needed to get a working implementation of the Opensearch Learning to Rank Plugin working locally with a dockerized version of Opensearch.

The best way to understand LTR is to read the official docs here.

Requirements

• Docker
• opensearch-plugin CLI tool
  • brew install opensearch

1. Set up Opensearch in Docker

NOTE: To use the OpenSearch image with a custom plugin, you must first create a Dockerfile. See

• Working with plugins (Opensearch)

• Installing (LTR official docs)

• Opensearch version: 2.5

• LTR Plugin v2.1.0 (compatible with OS 2.5). See plugin release history on GitHub.

Run:

• docker compose up
• curl http://localhost:9200 in a new terminal to check the cluster

2. Create index mapping and bulk index data

./bin/index.sh

Search for a movie to confirm it worked:

curl -X GET "http://localhost:9200/tmdb/_search?pretty=true" -H 'Content-Type: application/json' -d'
{
    "query": {
        "match": {
            "title": "First"
        }
    }
}'

3. Set up an LTR feature store

Set up default featureset index:

curl -X PUT "http://localhost:9200/_ltr?pretty=true" -H 'Content-Type: application/json'

Create a feature set called moviefeatureset:

curl -X POST "http://localhost:9200/_ltr/_featureset/moviefeatureset?pretty=true" -H 'Content-Type: application/json' -d'
{
   "featureset": {
        "features": [
            {
                "name": "title_query",
                "params": [
                    "query_text"
                ],
                "template_language": "mustache",
                "template": {
                    "match": {
                        "title": "{{query_text}}"
                    }
                }
            },
            {
                "name": "description_query",
                "params": [
                    "query_text"
                ],
                "template_language": "mustache",
                "template": {
                    "match": {
                        "description": "{{query_text}}"
                    }
                }
            }
        ]
   }
}'

Run curl http://localhost:9200/_ltr/_featureset?pretty=true to see registered features in the featureset.

4. Run a query to get logged features

First, run a simple text query:

curl -X GET "http://localhost:9200/tmdb/_search?pretty=true" -H 'Content-Type: application/json' -d'
{
    "query": {
        "match": {
            "title": "First"
        }
    }
}'

Now, run a query with an SLTR filter to get logged features back:

curl -X GET "http://localhost:9200/tmdb/_search?pretty=true" -H 'Content-Type: application/json' -d'
{
  "query": {
      "bool": {
        "filter" : [
            {
                "sltr" : {
                    "featureset" : "moviefeatureset",
                    "_name": "logged_featureset",
                    "active_features" : [ 
                        "title_query",
                        "description_query"
                    ],
                    "params": {
                        "query_text": "First"
                    }
                }
            }
        ]
      }
  },
  "ext": {
        "ltr_log": {
            "log_specs": {
                "name": "log_entry1",
                "named_query": "logged_featureset"
            }
        }
    }
}'

Result:

{
  "took": 2,
  "timed_out": false,
  "_shards": {
    "total": 1,
    "successful": 1,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": {
      "value": 1,
      "relation": "eq"
    },
    "max_score": 0.0,
    "hits": [
      {
        "_index": "movies",
        "_id": "rfzam40Bbf4EFOUV1cUr",
        "_score": 0.0,
        "_source": {
          "title": "First Blood",
          "year_released": 1982
        },
        "fields": {
          "_ltrlog": [
            {
              "log_entry1": [
                {
                  "name": "title_query",
                  "value": 0.2876821
                },
                {
                  "name": "description_query"
                }
              ]
            }
          ]
        },
        "matched_queries": [
          "logged_featureset"
        ]
      }
    ]
  }
}

Things to note:

• Logged feature values in "_ltrlog"
• Feature logging score returned for title_query
• Feature log result returned for description_query with no score. This is because we originally indexed a document without a description field.
• Nothing at all logged for year_released. This is because it was never registered as a feature of interest in the featureset.

Resources

• Elasticsearch Learning to Rank: the documentation
• Learning to Rank for Amazon OpenSearch Service (AWS)
• Working with plugins (Opensearch)
• Example LTR judgement list for movies