164 lines
6.5 KiB
Markdown
164 lines
6.5 KiB
Markdown
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---
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layout: default
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title: Explain
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parent: REST API reference
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nav_order: 140
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---
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# Explain
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Introduced 1.0
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{: .label .label-purple }
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Wondering why a specific document matches (or doesn’t match) a query? You can use the explain API for an explanation of how the relevance score (`_score`) is calculated for every result.
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OpenSearch uses a probabilistic ranking framework called [Okapi BM25](https://en.wikipedia.org/wiki/Okapi_BM25) to calculate relevance scores. Okapi BM25 is based on the original [TF/IDF](http://lucene.apache.org/core/4_0_0/core/org/apache/lucene/search/package-summary.html#scoring) framework used by Apache Lucene.
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The explain API is an expensive operation in terms of both resources and time. We recommend you don't use it for production workloads.
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{: .warning }
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## Example
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To see the explain output for all results, set the `explain` flag to `true` either in the URL or in the body of the request:
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```json
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POST kibana_sample_data_ecommerce/_search?explain=true
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{
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"query": {
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"match": {
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"customer_first_name": "Mary"
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}
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}
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}
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```
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To see the explain output for a specific document, specify the document ID in the URL:
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```json
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POST kibana_sample_data_ecommerce/_explain/EVz1Q3sBgg5eWQP6RSte
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{
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"query": {
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"match": {
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"customer_first_name": "Mary"
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}
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}
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}
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```
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## Path and HTTP methods
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```
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GET <target>/_explain/<id>
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POST <target>/_explain/<id>
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```
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## URL parameters
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You must specify the index and document ID. All other URL parameters are optional.
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Parameter | Type | Description | Required
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:--- | :--- | :--- | :---
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`<index>` | String | Name of the index. You can only specify a single index. | Yes
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`<_id>` | String | A unique identifier to attach to the document. | Yes
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`analyzer` | String | The analyzer to use in the query string. | No
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`analyze_wildcard` | Boolean | Specifies whether to analyze wildcard and prefix queries. Default is false. | No
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`default_operator` | String | Indicates whether the default operator for a string query should be AND or OR. Default is OR. | No
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`df` | String | The default field in case a field prefix is not provided in the query string. | No
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`lenient` | Boolean | Specifies whether OpenSearch should ignore format-based query failures (for example, querying a text field for an integer). Default is false. | No
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`preference` | String | Specifies a preference of which shard to retrieve results from. Available options are `_local`, which tells the operation to retrieve results from a locally allocated shard replica, and a custom string value assigned to a specific shard replica. By default, OpenSearch executes get document operations on random shards. | No
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`q` | String | Query in the Lucene query string syntax. | No
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`stored_fields` | Boolean | If true, the operation retrieves document fields stored in the index rather than the document’s `_source`. Default is false. | No
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`routing` | String | Value used to route the operation to a specific shard. | No
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`_source` | String | Whether to include the `_source` field in the response body. Default is true. | No
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`_source_excludes` | String | A comma-separated list of source fields to exclude in the query response. | No
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`_source_includes` | String | A comma-separated list of source fields to include in the query response. | No
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## Response
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```json
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{
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"_index" : "kibana_sample_data_ecommerce",
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"_type" : "_doc",
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"_id" : "EVz1Q3sBgg5eWQP6RSte",
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"matched" : true,
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"explanation" : {
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"value" : 3.5671005,
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"description" : "weight(customer_first_name:mary in 1) [PerFieldSimilarity], result of:",
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"details" : [
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{
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"value" : 3.5671005,
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"description" : "score(freq=1.0), computed as boost * idf * tf from:",
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"details" : [
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{
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"value" : 2.2,
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"description" : "boost",
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"details" : [ ]
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},
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{
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"value" : 3.4100041,
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"description" : "idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) from:",
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"details" : [
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{
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"value" : 154,
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"description" : "n, number of documents containing term",
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"details" : [ ]
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},
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{
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"value" : 4675,
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"description" : "N, total number of documents with field",
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"details" : [ ]
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}
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]
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},
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{
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"value" : 0.47548598,
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"description" : "tf, computed as freq / (freq + k1 * (1 - b + b * dl / avgdl)) from:",
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"details" : [
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{
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"value" : 1.0,
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"description" : "freq, occurrences of term within document",
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"details" : [ ]
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},
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{
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"value" : 1.2,
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"description" : "k1, term saturation parameter",
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"details" : [ ]
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},
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{
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"value" : 0.75,
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"description" : "b, length normalization parameter",
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"details" : [ ]
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},
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{
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"value" : 1.0,
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"description" : "dl, length of field",
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"details" : [ ]
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},
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{
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"value" : 1.1206417,
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"description" : "avgdl, average length of field",
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"details" : [ ]
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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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}
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```
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## Response body fields
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Field | Description
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:--- | :---
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`matched` | Indicates if the document is a match for the query.
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`explanation` | The `explanation` object has three properties: `value`, `description`, and `details`. The `value` shows the result of the calculation, the `description` explains what type of calculation is performed, and the `details` shows any subcalculations performed.
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Term frequency (`tf`) | How many times the term appears in a field for a given document. The more times the term occurs the higher is the relevance score.
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Inverse document frequency (`idf`) | How often the term appears within the index (across all the documents). The more often the term appears the lower the relevance score.
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Field normalization factor (`fieldNorm`) | The length of the field. OpenSearch assigns a higher relevance score to a term appearing in a relatively short field.
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The `tf`, `idf`, and `fieldNorm` values are calculated and stored at index time when a document is added or updated. The values might have some (typically small) inaccuracies as it’s based on summing the samples returned from each shard.
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Individual queries include other factors for calculating the relevance score, such as term proximity, fuzziness, and so on.
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