Compact and aligned text (CAT)

The compact and aligned text (CAT) APIs aim are intended only for human consumption using the Kibana console or command line. They are not intended for use by applications. For application consumption, it's recommend to use a corresponding JSON API. All the cat commands accept a query string parameter help to see all the headers and info they provide, and the /_cat command alone lists all the available commands.

Get CAT help Generally available

GET /_cat

Api key auth

Get help for the CAT APIs.

Responses

200 application/json

GET /_cat

curl \
 --request GET 'http://api.example.com/_cat' \
 --header "Authorization: $API_KEY"

Get datafeeds Generally available

GET /_cat/ml/datafeeds/{datafeed_id}

Api key auth

All methods and paths for this operation:

GET /_cat/ml/datafeeds

GET /_cat/ml/datafeeds/{datafeed_id}

Get configuration and usage information about datafeeds. This API returns a maximum of 10,000 datafeeds. If the Elasticsearch security features are enabled, you must have monitor_ml, monitor, manage_ml, or manage cluster privileges to use this API.

IMPORTANT: CAT APIs are only intended for human consumption using the Kibana console or command line. They are not intended for use by applications. For application consumption, use the get datafeed statistics API.

Required authorization

Cluster privileges: monitor_ml

Path parameters

datafeed_id string Required

A numerical character string that uniquely identifies the datafeed.

Query parameters

allow_no_match boolean
Specifies what to do when the request:
- Contains wildcard expressions and there are no datafeeds that match.
- Contains the _all string or no identifiers and there are no matches.
- Contains wildcard expressions and there are only partial matches.
If true, the API returns an empty datafeeds array when there are no matches and the subset of results when there are partial matches. If false, the API returns a 404 status code when there are no matches or only partial matches.
h string | array[string]
Comma-separated list of column names to display.

Supported values include:
- ae (or assignment_explanation): For started datafeeds only, contains messages relating to the selection of a node.
- bc (or buckets.count, bucketsCount): The number of buckets processed.
- id: A numerical character string that uniquely identifies the datafeed.
- na (or node.address, nodeAddress): For started datafeeds only, the network address of the node where the datafeed is started.
- ne (or node.ephemeral_id, nodeEphemeralId): For started datafeeds only, the ephemeral ID of the node where the datafeed is started.
- ni (or node.id, nodeId): For started datafeeds only, the unique identifier of the node where the datafeed is started.
- nn (or node.name, nodeName): For started datafeeds only, the name of the node where the datafeed is started.
- sba (or search.bucket_avg, searchBucketAvg): The average search time per bucket, in milliseconds.
- sc (or search.count, searchCount): The number of searches run by the datafeed.
- seah (or search.exp_avg_hour, searchExpAvgHour): The exponential average search time per hour, in milliseconds.
- st (or search.time, searchTime): The total time the datafeed spent searching, in milliseconds.
- s (or state): The status of the datafeed: starting, started, stopping, or stopped. If starting, the datafeed has been requested to start but has not yet started. If started, the datafeed is actively receiving data. If stopping, the datafeed has been requested to stop gracefully and is completing its final action. If stopped, the datafeed is stopped and will not receive data until it is re-started.
s string | array[string]
Comma-separated list of column names or column aliases used to sort the response.

Supported values include:
- ae (or assignment_explanation): For started datafeeds only, contains messages relating to the selection of a node.
- bc (or buckets.count, bucketsCount): The number of buckets processed.
- id: A numerical character string that uniquely identifies the datafeed.
- na (or node.address, nodeAddress): For started datafeeds only, the network address of the node where the datafeed is started.
- ne (or node.ephemeral_id, nodeEphemeralId): For started datafeeds only, the ephemeral ID of the node where the datafeed is started.
- ni (or node.id, nodeId): For started datafeeds only, the unique identifier of the node where the datafeed is started.
- nn (or node.name, nodeName): For started datafeeds only, the name of the node where the datafeed is started.
- sba (or search.bucket_avg, searchBucketAvg): The average search time per bucket, in milliseconds.
- sc (or search.count, searchCount): The number of searches run by the datafeed.
- seah (or search.exp_avg_hour, searchExpAvgHour): The exponential average search time per hour, in milliseconds.
- st (or search.time, searchTime): The total time the datafeed spent searching, in milliseconds.
- s (or state): The status of the datafeed: starting, started, stopping, or stopped. If starting, the datafeed has been requested to start but has not yet started. If started, the datafeed is actively receiving data. If stopping, the datafeed has been requested to stop gracefully and is completing its final action. If stopped, the datafeed is stopped and will not receive data until it is re-started.
time string

The unit used to display time values.

Values are nanos, micros, ms, s, m, h, or d.

Responses

200 application/json
Hide response attributes Show response attributes object
- id string
  
  The datafeed identifier.
- state string
  
  Values are started, stopped, starting, or stopping.
- assignment_explanation string
  
  For started datafeeds only, contains messages relating to the selection of a node.
- buckets.count string
  
  The number of buckets processed.
- search.count string
  
  The number of searches run by the datafeed.
- search.time string
  
  The total time the datafeed spent searching, in milliseconds.
- search.bucket_avg string
  
  The average search time per bucket, in milliseconds.
- search.exp_avg_hour string
  
  The exponential average search time per hour, in milliseconds.
- node.id string
  
  The unique identifier of the assigned node. For started datafeeds only, this information pertains to the node upon which the datafeed is started.
- node.name string
  
  The name of the assigned node. For started datafeeds only, this information pertains to the node upon which the datafeed is started.
- node.ephemeral_id string
  
  The ephemeral identifier of the assigned node. For started datafeeds only, this information pertains to the node upon which the datafeed is started.
- node.address string
  
  The network address of the assigned node. For started datafeeds only, this information pertains to the node upon which the datafeed is started.

GET /_cat/ml/datafeeds/{datafeed_id}

GET _cat/ml/datafeeds?v=true&format=json

resp = client.cat.ml_datafeeds(
    v=True,
    format="json",
)

const response = await client.cat.mlDatafeeds({
  v: "true",
  format: "json",
});

response = client.cat.ml_datafeeds(
  v: "true",
  format: "json"
)

$resp = $client->cat()->mlDatafeeds([
    "v" => "true",
    "format" => "json",
]);

curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_cat/ml/datafeeds?v=true&format=json"

client.cat().mlDatafeeds();

Response examples (200)

A successful response from `GET _cat/ml/datafeeds?v=true&format=json`.

[
  {
    "id": "datafeed-high_sum_total_sales",
    "state": "stopped",
    "buckets.count": "743",
    "search.count": "7"
  },
  {
    "id": "datafeed-low_request_rate",
    "state": "stopped",
    "buckets.count": "1457",
    "search.count": "3"
  },
  {
    "id": "datafeed-response_code_rates",
    "state": "stopped",
    "buckets.count": "1460",
    "search.count": "18"
  },
  {
    "id": "datafeed-url_scanning",
    "state": "stopped",
    "buckets.count": "1460",
    "search.count": "18"
  }
]

Get all connectors Beta

GET /_connector

Api key auth

Get information about all connectors.

Query parameters

from number

Starting offset (default: 0)
size number

Specifies a max number of results to get
index_name string | array[string]

A comma-separated list of connector index names to fetch connector documents for
connector_name string | array[string]

A comma-separated list of connector names to fetch connector documents for
service_type string | array[string]

A comma-separated list of connector service types to fetch connector documents for
include_deleted boolean

A flag to indicate if the desired connector should be fetched, even if it was soft-deleted.
query string

A wildcard query string that filters connectors with matching name, description or index name

Responses

200 application/json
Hide response attributes Show response attributes object
- count number Required
- results array[object] Required
  
  Hide results attributes Show results attributes object
  
  api_key_id string
  
  api_key_secret_id string
  
  configuration object Required
  
  Hide configuration attribute Show configuration attribute object
  
  * object Additional properties
  
  Hide * attributes Show * attributes object
  
  category string
  
  default_value number | string | boolean | null Required
  
  A scalar value.
  
  One of:
  number-1 number number-2 number string-3 string boolean-4 boolean string-5 string | null
  
  depends_on array[object] Required
  
  display string Required
  
  Values are textbox, textarea, numeric, toggle, or dropdown.
  
  label string Required
  
  options array[object] Required
  
  order number
  
  placeholder string
  
  required boolean Required
  
  sensitive boolean Required
  
  tooltip string | null
  
  One of:
  string-1 string string-2 string | null
  
  type string
  
  Values are str, int, list, or bool.
  
  ui_restrictions array[string]
  
  validations array[object]
  
  value object Required
  
  custom_scheduling object Required
  
  Hide custom_scheduling attribute Show custom_scheduling attribute object
  
  * object Additional properties
  
  Hide * attributes Show * attributes object
  
  configuration_overrides object Required
  
  Hide configuration_overrides attributes Show configuration_overrides attributes object
  
  max_crawl_depth number
  
  sitemap_discovery_disabled boolean
  
  domain_allowlist array[string]
  
  sitemap_urls array[string]
  
  seed_urls array[string]
  
  enabled boolean Required
  
  interval string Required
  
  last_synced string
  
  name string Required
  
  deleted boolean Required
  
  description string
  
  error string | null
  
  One of:
  string-1 string string-2 string | null
  
  features object
  
  Hide features attributes Show features attributes object
  
  document_level_security object
  
  Hide document_level_security attribute Show document_level_security attribute object
  
  enabled boolean Required
  
  incremental_sync object
  
  Hide incremental_sync attribute Show incremental_sync attribute object
  
  enabled boolean Required
  
  native_connector_api_keys object
  
  Hide native_connector_api_keys attribute Show native_connector_api_keys attribute object
  
  enabled boolean Required
  
  sync_rules object
  
  Hide sync_rules attributes Show sync_rules attributes object
  
  advanced object
  
  Hide advanced attribute Show advanced attribute object
  
  enabled boolean Required
  
  basic object
  
  Hide basic attribute Show basic attribute object
  
  enabled boolean Required
  
  filtering array[object] Required
  
  Hide filtering attributes Show filtering attributes object
  
  active object Required
  
  Hide active attributes Show active attributes object
  
  advanced_snippet object Required
  
  rules array[object] Required
  
  validation object Required
  
  domain string
  
  draft object Required
  
  Hide draft attributes Show draft attributes object
  
  advanced_snippet object Required
  
  rules array[object] Required
  
  validation object Required
  
  id string
  
  index_name string | null
  
  One of:
  IndexName string string-2 string | null
  
  is_native boolean Required
  
  language string
  
  last_access_control_sync_error string
  
  last_access_control_sync_scheduled_at string | number
  
  A date and time, either as a string whose format can depend on the context (defaulting to ISO 8601), or a number of milliseconds since the Epoch. Elasticsearch accepts both as input, but will generally output a string representation.
  
  One of:
  string-1 string UnitMillis number
  
  last_access_control_sync_status string
  
  Values are canceling, canceled, completed, error, in_progress, pending, or suspended.
  
  last_deleted_document_count number
  
  last_incremental_sync_scheduled_at string | number
  
  A date and time, either as a string whose format can depend on the context (defaulting to ISO 8601), or a number of milliseconds since the Epoch. Elasticsearch accepts both as input, but will generally output a string representation.
  
  One of:
  string-1 string UnitMillis number
  
  last_indexed_document_count number
  
  last_seen string | number
  
  A date and time, either as a string whose format can depend on the context (defaulting to ISO 8601), or a number of milliseconds since the Epoch. Elasticsearch accepts both as input, but will generally output a string representation.
  
  One of:
  string-1 string UnitMillis number
  
  last_sync_error string
  
  last_sync_scheduled_at string | number
  
  A date and time, either as a string whose format can depend on the context (defaulting to ISO 8601), or a number of milliseconds since the Epoch. Elasticsearch accepts both as input, but will generally output a string representation.
  
  One of:
  string-1 string UnitMillis number
  
  last_sync_status string
  
  Values are canceling, canceled, completed, error, in_progress, pending, or suspended.
  
  last_synced string | number
  
  A date and time, either as a string whose format can depend on the context (defaulting to ISO 8601), or a number of milliseconds since the Epoch. Elasticsearch accepts both as input, but will generally output a string representation.
  
  One of:
  string-1 string UnitMillis number
  
  name string
  
  pipeline object
  
  Hide pipeline attributes Show pipeline attributes object
  
  extract_binary_content boolean Required
  
  name string Required
  
  reduce_whitespace boolean Required
  
  run_ml_inference boolean Required
  
  scheduling object Required
  
  Hide scheduling attributes Show scheduling attributes object
  
  access_control object
  
  Hide access_control attributes Show access_control attributes object
  
  enabled boolean Required
  
  interval string Required
  
  The interval is expressed using the crontab syntax
  
  full object
  
  Hide full attributes Show full attributes object
  
  enabled boolean Required
  
  interval string Required
  
  The interval is expressed using the crontab syntax
  
  incremental object
  
  Hide incremental attributes Show incremental attributes object
  
  enabled boolean Required
  
  interval string Required
  
  The interval is expressed using the crontab syntax
  
  service_type string
  
  status string Required
  
  Values are created, needs_configuration, configured, connected, or error.
  
  sync_cursor object
  
  sync_now boolean Required

GET /_connector

GET _connector

resp = client.connector.list()

const response = await client.connector.list();

response = client.connector.list

$resp = $client->connector()->list();

curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_connector"

client.connector().list(l -> l);

Create a connector Beta

POST /_connector

Api key auth

Connectors are Elasticsearch integrations that bring content from third-party data sources, which can be deployed on Elastic Cloud or hosted on your own infrastructure. Elastic managed connectors (Native connectors) are a managed service on Elastic Cloud. Self-managed connectors (Connector clients) are self-managed on your infrastructure.

application/json

Body

description string
index_name string
is_native boolean
language string
name string
service_type string

Responses

200 application/json
Hide response attributes Show response attributes object
- result string Required
  
  Values are created, updated, deleted, not_found, or noop.
- id string Required

POST /_connector

curl \
 --request POST 'http://api.example.com/_connector' \
 --header "Authorization: $API_KEY" \
 --header "Content-Type: application/json" \
 --data '{"description":"string","index_name":"string","is_native":true,"language":"string","name":"string","service_type":"string"}'

Update the connector configuration Beta

PUT /_connector/{connector_id}/_configuration

Api key auth

Update the configuration field in the connector document.

Path parameters

connector_id string Required

The unique identifier of the connector to be updated

application/json

Body Required

configuration object
Hide configuration attribute Show configuration attribute object
- * object Additional properties
  Hide * attributes Show * attributes object
  
  category string
  
  default_value number | string | boolean | null Required
  
  A scalar value.
  
  One of:
  number-1 number number-2 number string-3 string boolean-4 boolean string-5 string | null
  
  depends_on array[object] Required
  
  Hide depends_on attributes Show depends_on attributes object
  
  field string Required
  
  value number | string | boolean | null Required
  
  A scalar value.
  
  One of:
  number-1 number number-2 number string-3 string boolean-4 boolean string-5 string | null
  
  display string Required
  
  Values are textbox, textarea, numeric, toggle, or dropdown.
  
  label string Required
  
  options array[object] Required
  
  Hide options attributes Show options attributes object
  
  label string Required
  
  value number | string | boolean | null Required
  
  A scalar value.
  
  One of:
  number-1 number number-2 number string-3 string boolean-4 boolean string-5 string | null
  
  order number
  
  placeholder string
  
  required boolean Required
  
  sensitive boolean Required
  
  tooltip string | null
  
  One of:
  string-1 string string-2 string | null
  
  type string
  
  Values are str, int, list, or bool.
  
  ui_restrictions array[string]
  
  validations array[object]
  
  One of:
  LessThanValidation object GreaterThanValidation object ListTypeValidation object IncludedInValidation object RegexValidation object
  
  Hide attributes Show attributes
  
  type string Required Discriminator
  
  constraint number Required
  
  value object Required
values object
Hide values attribute Show values attribute object
- * object Additional properties

Responses

200 application/json
Hide response attribute Show response attribute object
- result string Required
  
  Values are created, updated, deleted, not_found, or noop.

PUT /_connector/{connector_id}/_configuration

PUT _connector/my-spo-connector/_configuration
{
    "values": {
        "tenant_id": "my-tenant-id",
        "tenant_name": "my-sharepoint-site",
        "client_id": "foo",
        "secret_value": "bar",
        "site_collections": "*"
    }
}

resp = client.connector.update_configuration(
    connector_id="my-spo-connector",
    values={
        "tenant_id": "my-tenant-id",
        "tenant_name": "my-sharepoint-site",
        "client_id": "foo",
        "secret_value": "bar",
        "site_collections": "*"
    },
)

const response = await client.connector.updateConfiguration({
  connector_id: "my-spo-connector",
  values: {
    tenant_id: "my-tenant-id",
    tenant_name: "my-sharepoint-site",
    client_id: "foo",
    secret_value: "bar",
    site_collections: "*",
  },
});

response = client.connector.update_configuration(
  connector_id: "my-spo-connector",
  body: {
    "values": {
      "tenant_id": "my-tenant-id",
      "tenant_name": "my-sharepoint-site",
      "client_id": "foo",
      "secret_value": "bar",
      "site_collections": "*"
    }
  }
)

$resp = $client->connector()->updateConfiguration([
    "connector_id" => "my-spo-connector",
    "body" => [
        "values" => [
            "tenant_id" => "my-tenant-id",
            "tenant_name" => "my-sharepoint-site",
            "client_id" => "foo",
            "secret_value" => "bar",
            "site_collections" => "*",
        ],
    ],
]);

curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"values":{"tenant_id":"my-tenant-id","tenant_name":"my-sharepoint-site","client_id":"foo","secret_value":"bar","site_collections":"*"}}' "$ELASTICSEARCH_URL/_connector/my-spo-connector/_configuration"

client.connector().updateConfiguration(u -> u
    .connectorId("my-spo-connector")
    .values(Map.of("tenant_id", JsonData.fromJson("\"my-tenant-id\""),"tenant_name", JsonData.fromJson("\"my-sharepoint-site\""),"secret_value", JsonData.fromJson("\"bar\""),"client_id", JsonData.fromJson("\"foo\""),"site_collections", JsonData.fromJson("\"*\"")))
);

Request examples

{
    "values": {
        "tenant_id": "my-tenant-id",
        "tenant_name": "my-sharepoint-site",
        "client_id": "foo",
        "secret_value": "bar",
        "site_collections": "*"
    }
}

{
    "values": {
        "secret_value": "foo-bar"
    }
}

Response examples (200)

{
  "result": "updated"
}

Update the connector filtering Beta

PUT /_connector/{connector_id}/_filtering

Api key auth

Update the draft filtering configuration of a connector and marks the draft validation state as edited. The filtering draft is activated once validated by the running Elastic connector service. The filtering property is used to configure sync rules (both basic and advanced) for a connector.

Path parameters

connector_id string Required

The unique identifier of the connector to be updated

application/json

Body Required

filtering array[object]
Hide filtering attributes Show filtering attributes object
- active object Required
  Hide active attributes Show active attributes object
  
  advanced_snippet object Required
  
  Hide advanced_snippet attributes Show advanced_snippet attributes object
  
  created_at string | number
  
  A date and time, either as a string whose format can depend on the context (defaulting to ISO 8601), or a number of milliseconds since the Epoch. Elasticsearch accepts both as input, but will generally output a string representation.
  
  One of:
  string-1 string UnitMillis number
  
  updated_at string | number
  
  A date and time, either as a string whose format can depend on the context (defaulting to ISO 8601), or a number of milliseconds since the Epoch. Elasticsearch accepts both as input, but will generally output a string representation.
  
  One of:
  string-1 string UnitMillis number
  
  value object Required
  
  rules array[object] Required
  
  Hide rules attributes Show rules attributes object
  
  created_at string
  
  field string Required
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  id string Required
  
  order number Required
  
  policy string Required
  
  Values are exclude or include.
  
  rule string Required
  
  Values are contains, ends_with, equals, regex, starts_with, >, or <.
  
  updated_at string
  
  value string Required
  
  validation object Required
  
  Hide validation attributes Show validation attributes object
  
  errors array[object] Required
  
  Hide errors attributes Show errors attributes object
  
  ids array[string] Required
  
  messages array[string] Required
  
  state string Required
  
  Values are edited, invalid, or valid.
- domain string
- draft object Required
  Hide draft attributes Show draft attributes object
  
  advanced_snippet object Required
  
  Hide advanced_snippet attributes Show advanced_snippet attributes object
  
  created_at string | number
  
  A date and time, either as a string whose format can depend on the context (defaulting to ISO 8601), or a number of milliseconds since the Epoch. Elasticsearch accepts both as input, but will generally output a string representation.
  
  One of:
  string-1 string UnitMillis number
  
  updated_at string | number
  
  A date and time, either as a string whose format can depend on the context (defaulting to ISO 8601), or a number of milliseconds since the Epoch. Elasticsearch accepts both as input, but will generally output a string representation.
  
  One of:
  string-1 string UnitMillis number
  
  value object Required
  
  rules array[object] Required
  
  Hide rules attributes Show rules attributes object
  
  created_at string
  
  field string Required
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  id string Required
  
  order number Required
  
  policy string Required
  
  Values are exclude or include.
  
  rule string Required
  
  Values are contains, ends_with, equals, regex, starts_with, >, or <.
  
  updated_at string
  
  value string Required
  
  validation object Required
  
  Hide validation attributes Show validation attributes object
  
  errors array[object] Required
  
  Hide errors attributes Show errors attributes object
  
  ids array[string] Required
  
  messages array[string] Required
  
  state string Required
  
  Values are edited, invalid, or valid.
rules array[object]
Hide rules attributes Show rules attributes object
- created_at string | number
  
  A date and time, either as a string whose format can depend on the context (defaulting to ISO 8601), or a number of milliseconds since the Epoch. Elasticsearch accepts both as input, but will generally output a string representation.
  
  One of:
  string-1 string UnitMillis number
- field string Required
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
- id string Required
- order number Required
- policy string Required
  
  Values are exclude or include.
- rule string Required
  
  Values are contains, ends_with, equals, regex, starts_with, >, or <.
- updated_at string | number
  
  A date and time, either as a string whose format can depend on the context (defaulting to ISO 8601), or a number of milliseconds since the Epoch. Elasticsearch accepts both as input, but will generally output a string representation.
  
  One of:
  string-1 string UnitMillis number
- value string Required
advanced_snippet object
Hide advanced_snippet attributes Show advanced_snippet attributes object
- created_at string | number
  
  A date and time, either as a string whose format can depend on the context (defaulting to ISO 8601), or a number of milliseconds since the Epoch. Elasticsearch accepts both as input, but will generally output a string representation.
  
  One of:
  string-1 string UnitMillis number
- updated_at string | number
  
  A date and time, either as a string whose format can depend on the context (defaulting to ISO 8601), or a number of milliseconds since the Epoch. Elasticsearch accepts both as input, but will generally output a string representation.
  
  One of:
  string-1 string UnitMillis number
- value object Required

Responses

200 application/json
Hide response attribute Show response attribute object
- result string Required
  
  Values are created, updated, deleted, not_found, or noop.

PUT /_connector/{connector_id}/_filtering

PUT _connector/my-g-drive-connector/_filtering
{
    "rules": [
         {
            "field": "file_extension",
            "id": "exclude-txt-files",
            "order": 0,
            "policy": "exclude",
            "rule": "equals",
            "value": "txt"
        },
        {
            "field": "_",
            "id": "DEFAULT",
            "order": 1,
            "policy": "include",
            "rule": "regex",
            "value": ".*"
        }
    ]
}

resp = client.connector.update_filtering(
    connector_id="my-g-drive-connector",
    rules=[
        {
            "field": "file_extension",
            "id": "exclude-txt-files",
            "order": 0,
            "policy": "exclude",
            "rule": "equals",
            "value": "txt"
        },
        {
            "field": "_",
            "id": "DEFAULT",
            "order": 1,
            "policy": "include",
            "rule": "regex",
            "value": ".*"
        }
    ],
)

const response = await client.connector.updateFiltering({
  connector_id: "my-g-drive-connector",
  rules: [
    {
      field: "file_extension",
      id: "exclude-txt-files",
      order: 0,
      policy: "exclude",
      rule: "equals",
      value: "txt",
    },
    {
      field: "_",
      id: "DEFAULT",
      order: 1,
      policy: "include",
      rule: "regex",
      value: ".*",
    },
  ],
});

response = client.connector.update_filtering(
  connector_id: "my-g-drive-connector",
  body: {
    "rules": [
      {
        "field": "file_extension",
        "id": "exclude-txt-files",
        "order": 0,
        "policy": "exclude",
        "rule": "equals",
        "value": "txt"
      },
      {
        "field": "_",
        "id": "DEFAULT",
        "order": 1,
        "policy": "include",
        "rule": "regex",
        "value": ".*"
      }
    ]
  }
)

$resp = $client->connector()->updateFiltering([
    "connector_id" => "my-g-drive-connector",
    "body" => [
        "rules" => array(
            [
                "field" => "file_extension",
                "id" => "exclude-txt-files",
                "order" => 0,
                "policy" => "exclude",
                "rule" => "equals",
                "value" => "txt",
            ],
            [
                "field" => "_",
                "id" => "DEFAULT",
                "order" => 1,
                "policy" => "include",
                "rule" => "regex",
                "value" => ".*",
            ],
        ),
    ],
]);

curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"rules":[{"field":"file_extension","id":"exclude-txt-files","order":0,"policy":"exclude","rule":"equals","value":"txt"},{"field":"_","id":"DEFAULT","order":1,"policy":"include","rule":"regex","value":".*"}]}' "$ELASTICSEARCH_URL/_connector/my-g-drive-connector/_filtering"

client.connector().updateFiltering(u -> u
    .connectorId("my-g-drive-connector")
    .rules(List.of(FilteringRule.of(f -> f
            .field("file_extension")
            .id("exclude-txt-files")
            .order(0)
            .policy(FilteringPolicy.Exclude)
            .rule(FilteringRuleRule.Equals)
            .value("txt")),FilteringRule.of(f -> f
            .field("_")
            .id("DEFAULT")
            .order(1)
            .policy(FilteringPolicy.Include)
            .rule(FilteringRuleRule.Regex)
            .value(".*"))))
);

Request examples

{
    "rules": [
         {
            "field": "file_extension",
            "id": "exclude-txt-files",
            "order": 0,
            "policy": "exclude",
            "rule": "equals",
            "value": "txt"
        },
        {
            "field": "_",
            "id": "DEFAULT",
            "order": 1,
            "policy": "include",
            "rule": "regex",
            "value": ".*"
        }
    ]
}

{
    "advanced_snippet": {
        "value": [{
            "tables": [
                "users",
                "orders"
            ],
            "query": "SELECT users.id AS id, orders.order_id AS order_id FROM users JOIN orders ON users.id = orders.user_id"
        }]
    }
}

Response examples (200)

{
  "result": "updated"
}

Update the connector pipeline Beta

PUT /_connector/{connector_id}/_pipeline

Api key auth

When you create a new connector, the configuration of an ingest pipeline is populated with default settings.

Path parameters

connector_id string Required

The unique identifier of the connector to be updated

application/json

Body Required

pipeline object Required
Hide pipeline attributes Show pipeline attributes object
- extract_binary_content boolean Required
- name string Required
- reduce_whitespace boolean Required
- run_ml_inference boolean Required

Responses

200 application/json
Hide response attribute Show response attribute object
- result string Required
  
  Values are created, updated, deleted, not_found, or noop.

PUT /_connector/{connector_id}/_pipeline

PUT _connector/my-connector/_pipeline
{
    "pipeline": {
        "extract_binary_content": true,
        "name": "my-connector-pipeline",
        "reduce_whitespace": true,
        "run_ml_inference": true
    }
}

resp = client.connector.update_pipeline(
    connector_id="my-connector",
    pipeline={
        "extract_binary_content": True,
        "name": "my-connector-pipeline",
        "reduce_whitespace": True,
        "run_ml_inference": True
    },
)

const response = await client.connector.updatePipeline({
  connector_id: "my-connector",
  pipeline: {
    extract_binary_content: true,
    name: "my-connector-pipeline",
    reduce_whitespace: true,
    run_ml_inference: true,
  },
});

response = client.connector.update_pipeline(
  connector_id: "my-connector",
  body: {
    "pipeline": {
      "extract_binary_content": true,
      "name": "my-connector-pipeline",
      "reduce_whitespace": true,
      "run_ml_inference": true
    }
  }
)

$resp = $client->connector()->updatePipeline([
    "connector_id" => "my-connector",
    "body" => [
        "pipeline" => [
            "extract_binary_content" => true,
            "name" => "my-connector-pipeline",
            "reduce_whitespace" => true,
            "run_ml_inference" => true,
        ],
    ],
]);

curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"pipeline":{"extract_binary_content":true,"name":"my-connector-pipeline","reduce_whitespace":true,"run_ml_inference":true}}' "$ELASTICSEARCH_URL/_connector/my-connector/_pipeline"

client.connector().updatePipeline(u -> u
    .connectorId("my-connector")
    .pipeline(p -> p
        .extractBinaryContent(true)
        .name("my-connector-pipeline")
        .reduceWhitespace(true)
        .runMlInference(true)
    )
);

Request example

{
    "pipeline": {
        "extract_binary_content": true,
        "name": "my-connector-pipeline",
        "reduce_whitespace": true,
        "run_ml_inference": true
    }
}

Response examples (200)

{
  "result": "updated"
}

Update the connector service type Beta

PUT /_connector/{connector_id}/_service_type

Api key auth

Path parameters

connector_id string Required

The unique identifier of the connector to be updated

application/json

Body Required

service_type string Required

Responses

200 application/json
Hide response attribute Show response attribute object
- result string Required
  
  Values are created, updated, deleted, not_found, or noop.

PUT /_connector/{connector_id}/_service_type

PUT _connector/my-connector/_service_type
{
    "service_type": "sharepoint_online"
}

resp = client.connector.update_service_type(
    connector_id="my-connector",
    service_type="sharepoint_online",
)

const response = await client.connector.updateServiceType({
  connector_id: "my-connector",
  service_type: "sharepoint_online",
});

response = client.connector.update_service_type(
  connector_id: "my-connector",
  body: {
    "service_type": "sharepoint_online"
  }
)

$resp = $client->connector()->updateServiceType([
    "connector_id" => "my-connector",
    "body" => [
        "service_type" => "sharepoint_online",
    ],
]);

curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service_type":"sharepoint_online"}' "$ELASTICSEARCH_URL/_connector/my-connector/_service_type"

client.connector().updateServiceType(u -> u
    .connectorId("my-connector")
    .serviceType("sharepoint_online")
);

Request example

{
    "service_type": "sharepoint_online"
}

Response examples (200)

{
  "result": "updated"
}

Update the connector status Technical preview

PUT /_connector/{connector_id}/_status

Api key auth

Path parameters

connector_id string Required

The unique identifier of the connector to be updated

application/json

Body Required

status string Required

Values are created, needs_configuration, configured, connected, or error.

Responses

200 application/json
Hide response attribute Show response attribute object
- result string Required
  
  Values are created, updated, deleted, not_found, or noop.

PUT /_connector/{connector_id}/_status

PUT _connector/my-connector/_status
{
    "status": "needs_configuration"
}

resp = client.connector.update_status(
    connector_id="my-connector",
    status="needs_configuration",
)

const response = await client.connector.updateStatus({
  connector_id: "my-connector",
  status: "needs_configuration",
});

response = client.connector.update_status(
  connector_id: "my-connector",
  body: {
    "status": "needs_configuration"
  }
)

$resp = $client->connector()->updateStatus([
    "connector_id" => "my-connector",
    "body" => [
        "status" => "needs_configuration",
    ],
]);

curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"status":"needs_configuration"}' "$ELASTICSEARCH_URL/_connector/my-connector/_status"

client.connector().updateStatus(u -> u
    .connectorId("my-connector")
    .status(ConnectorStatus.NeedsConfiguration)
);

Request example

{
    "status": "needs_configuration"
}

Response examples (200)

{
  "result": "updated"
}

Update data stream lifecycles Generally available

PUT /_data_stream/{name}/_lifecycle

Api key auth

Update the data stream lifecycle of the specified data streams.

External documentation

Path parameters

name string | array[string] Required

Comma-separated list of data streams used to limit the request. Supports wildcards (*). To target all data streams use * or _all.

Query parameters

expand_wildcards string | array[string]
Type of data stream that wildcard patterns can match. Supports comma-separated values, such as open,hidden.

Supported values include:
- all: Match any data stream or index, including hidden ones.
- open: Match open, non-hidden indices. Also matches any non-hidden data stream.
- closed: Match closed, non-hidden indices. Also matches any non-hidden data stream. Data streams cannot be closed.
- hidden: Match hidden data streams and hidden indices. Must be combined with open, closed, or both.
- none: Wildcard expressions are not accepted.
Values are all, open, closed, hidden, or none.
master_timeout string

Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.

Values are -1 or 0.
timeout string

Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.

Values are -1 or 0.

application/json

Body

data_retention string

A duration. Units can be nanos, micros, ms (milliseconds), s (seconds), m (minutes), h (hours) and d (days). Also accepts "0" without a unit and "-1" to indicate an unspecified value.
downsampling object
Hide downsampling attribute Show downsampling attribute object
- rounds array[object] Required
  
  The list of downsampling rounds to execute as part of this downsampling configuration
  Hide rounds attributes Show rounds attributes object
  
  after string Required
  
  A duration. Units can be nanos, micros, ms (milliseconds), s (seconds), m (minutes), h (hours) and d (days). Also accepts "0" without a unit and "-1" to indicate an unspecified value.
  
  config object Required
  
  Hide config attribute Show config attribute object
  
  fixed_interval string Required
  
  A date histogram interval. Similar to Duration with additional units: w (week), M (month), q (quarter) and y (year)
enabled boolean

If defined, it turns data stream lifecycle on/off (true/false) for this data stream. A data stream lifecycle that's disabled (enabled: false) will have no effect on the data stream.

Default value is true.

Responses

200 application/json
Hide response attribute Show response attribute object
- acknowledged boolean Required
  
  For a successful response, this value is always true. On failure, an exception is returned instead.

PUT /_data_stream/{name}/_lifecycle

PUT _data_stream/my-data-stream/_lifecycle
{
  "data_retention": "7d"
}

resp = client.indices.put_data_lifecycle(
    name="my-data-stream",
    data_retention="7d",
)

const response = await client.indices.putDataLifecycle({
  name: "my-data-stream",
  data_retention: "7d",
});

response = client.indices.put_data_lifecycle(
  name: "my-data-stream",
  body: {
    "data_retention": "7d"
  }
)

$resp = $client->indices()->putDataLifecycle([
    "name" => "my-data-stream",
    "body" => [
        "data_retention" => "7d",
    ],
]);

curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"data_retention":"7d"}' "$ELASTICSEARCH_URL/_data_stream/my-data-stream/_lifecycle"

client.indices().putDataLifecycle(p -> p
    .dataRetention(d -> d
        .time("7d")
    )
    .name("my-data-stream")
);

Request examples

{
  "data_retention": "7d"
}

This example configures two downsampling rounds.

{
    "downsampling": [
      {
        "after": "1d",
        "fixed_interval": "10m"
      },
      {
        "after": "7d",
        "fixed_interval": "1d"
      }
    ]
}

Response examples (200)

A successful response for configuring a data stream lifecycle.

{
  "acknowledged": true
}

Get EQL search results Generally available

POST /{index}/_eql/search

Api key auth

All methods and paths for this operation:

GET /{index}/_eql/search

POST /{index}/_eql/search

Returns search results for an Event Query Language (EQL) query. EQL assumes each document in a data stream or index corresponds to an event.

External documentation

Path parameters

index string | array[string] Required

The name of the index to scope the operation

Query parameters

allow_no_indices boolean

Whether to ignore if a wildcard indices expression resolves into no concrete indices. (This includes _all string or when no indices have been specified)
allow_partial_search_results boolean

If true, returns partial results if there are shard failures. If false, returns an error with no partial results.
allow_partial_sequence_results boolean

If true, sequence queries will return partial results in case of shard failures. If false, they will return no results at all. This flag has effect only if allow_partial_search_results is true.
expand_wildcards string | array[string]
Whether to expand wildcard expression to concrete indices that are open, closed or both.

Supported values include:
- all: Match any data stream or index, including hidden ones.
- open: Match open, non-hidden indices. Also matches any non-hidden data stream.
- closed: Match closed, non-hidden indices. Also matches any non-hidden data stream. Data streams cannot be closed.
- hidden: Match hidden data streams and hidden indices. Must be combined with open, closed, or both.
- none: Wildcard expressions are not accepted.
Values are all, open, closed, hidden, or none.
ccs_minimize_roundtrips boolean

Indicates whether network round-trips should be minimized as part of cross-cluster search requests execution
ignore_unavailable boolean

If true, missing or closed indices are not included in the response.
keep_alive string

Period for which the search and its results are stored on the cluster.

Values are -1 or 0.
keep_on_completion boolean

If true, the search and its results are stored on the cluster.
wait_for_completion_timeout string

Timeout duration to wait for the request to finish. Defaults to no timeout, meaning the request waits for complete search results.

Values are -1 or 0.

application/json

Body Required

query string Required

EQL query you wish to run.
case_sensitive boolean
event_category_field string

Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
tiebreaker_field string

Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
timestamp_field string

Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
fetch_size number
filter object | array[object]

Query, written in Query DSL, used to filter the events on which the EQL query runs.

One of:
QueryContainer object array-2 array[object]

An Elasticsearch Query DSL (Domain Specific Language) object that defines a query.

External documentation
keep_alive string

A duration. Units can be nanos, micros, ms (milliseconds), s (seconds), m (minutes), h (hours) and d (days). Also accepts "0" without a unit and "-1" to indicate an unspecified value.
keep_on_completion boolean
wait_for_completion_timeout string

A duration. Units can be nanos, micros, ms (milliseconds), s (seconds), m (minutes), h (hours) and d (days). Also accepts "0" without a unit and "-1" to indicate an unspecified value.
allow_partial_search_results boolean

Allow query execution also in case of shard failures. If true, the query will keep running and will return results based on the available shards. For sequences, the behavior can be further refined using allow_partial_sequence_results

Default value is true.
allow_partial_sequence_results boolean

This flag applies only to sequences and has effect only if allow_partial_search_results=true. If true, the sequence query will return results based on the available shards, ignoring the others. If false, the sequence query will return successfully, but will always have empty results.

Default value is false.
size number
fields object | array[object]

Array of wildcard (*) patterns. The response returns values for field names matching these patterns in the fields property of each hit.
One of:
FieldAndFormat object array-2 array[object]
A reference to a field with formatting instructions on how to return the value
Hide attributes Show attributes

field string Required

Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.

format string

The format in which the values are returned.

include_unmapped boolean
A reference to a field with formatting instructions on how to return the value
Hide attributes Show attributes object

field string Required

Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.

format string

The format in which the values are returned.

include_unmapped boolean
result_position string

Values are tail or head.
runtime_mappings object
Hide runtime_mappings attribute Show runtime_mappings attribute object
- * object Additional properties
  Hide * attributes Show * attributes object
  
  fields object
  
  For type composite
  
  Hide fields attribute Show fields attribute object
  
  * object Additional properties
  
  Hide * attribute Show * attribute object
  
  type string Required
  
  Values are boolean, composite, date, double, geo_point, geo_shape, ip, keyword, long, or lookup.
  
  fetch_fields array[object]
  
  For type lookup
  
  Hide fetch_fields attributes Show fetch_fields attributes object
  
  field string Required
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  format string
  
  format string
  
  A custom format for date type runtime fields.
  
  input_field string
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  target_field string
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  target_index string
  
  script object
  
  Hide script attributes Show script attributes object
  
  source string | object
  
  One of:
  string-1 string SearchRequestBody object
  
  Hide attributes Show attributes
  
  aggregations object
  
  Defines the aggregations that are run as part of the search request.
  
  External documentation
  
  collapse object
  External documentation
  
  explain boolean
  
  If true, the request returns detailed information about score computation as part of a hit.
  
  Default value is false.
  
  ext object
  
  Configuration of search extensions defined by Elasticsearch plugins.
  
  Hide ext attribute Show ext attribute object
  
  * object Additional properties
  
  from number
  
  The starting document offset, which must be non-negative. By default, you cannot page through more than 10,000 hits using the from and size parameters. To page through more hits, use the search_after parameter.
  
  Default value is 0.
  
  highlight object
  
  track_total_hits boolean | number
  
  Number of hits matching the query to count accurately. If true, the exact number of hits is returned at the cost of some performance. If false, the response does not include the total number of hits matching the query. Defaults to 10,000 hits.
  
  indices_boost array[object]
  
  Boost the _score of documents from specified indices. The boost value is the factor by which scores are multiplied. A boost value greater than 1.0 increases the score. A boost value between 0 and 1.0 decreases the score.
  
  External documentation
  
  docvalue_fields array[object]
  
  An array of wildcard (*) field patterns. The request returns doc values for field names matching these patterns in the hits.fields property of the response.
  
  A reference to a field with formatting instructions on how to return the value
  
  External documentation
  
  A reference to a field with formatting instructions on how to return the value
  
  knn object | array[object]
  
  The approximate kNN search to run.
  
  One of:
  KnnSearch object array-2 array[object]
  
  min_score number
  
  The minimum _score for matching documents. Documents with a lower _score are not included in search results or results collected by aggregations.
  
  post_filter object
  
  An Elasticsearch Query DSL (Domain Specific Language) object that defines a query.
  
  External documentation
  
  profile boolean
  
  Set to true to return detailed timing information about the execution of individual components in a search request. NOTE: This is a debugging tool and adds significant overhead to search execution.
  
  Default value is false.
  
  query object
  
  An Elasticsearch Query DSL (Domain Specific Language) object that defines a query.
  
  External documentation
  
  rescore array[object]
  
  retriever object
  
  Hide retriever attributes Show retriever attributes object
  
  standard
  
  knn
  
  rrf
  
  text_similarity_reranker
  
  rule
  
  rescorer
  
  linear
  
  pinned
  
  script_fields object
  
  Retrieve a script evaluation (based on different fields) for each hit.
  
  Hide script_fields attribute Show script_fields attribute object
  
  * object Additional properties
  
  search_after array[number | string | boolean | null]
  
  A field value.
  
  size number
  
  The number of hits to return, which must not be negative. By default, you cannot page through more than 10,000 hits using the from and size parameters. To page through more hits, use the search_after property.
  
  Default value is 10.
  
  slice object
  
  Hide slice attributes Show slice attributes object
  
  field string
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  id string Required
  
  max number Required
  
  sort array[string | object]
  
  _source boolean | object
  
  Defines how to fetch a source. Fetching can be disabled entirely, or the source can be filtered.
  
  One of:
  boolean-1 boolean SourceFilter object
  
  fields array[object]
  
  An array of wildcard (*) field patterns. The request returns values for field names matching these patterns in the hits.fields property of the response.
  
  A reference to a field with formatting instructions on how to return the value
  
  A reference to a field with formatting instructions on how to return the value
  
  suggest object
  
  Hide suggest attribute Show suggest attribute object
  
  text string
  
  Global suggest text, to avoid repetition when the same text is used in several suggesters
  
  terminate_after number
  
  The maximum number of documents to collect for each shard. If a query reaches this limit, Elasticsearch terminates the query early. Elasticsearch collects documents before sorting.
  
  IMPORTANT: Use with caution. Elasticsearch applies this property to each shard handling the request. When possible, let Elasticsearch perform early termination automatically. Avoid specifying this property for requests that target data streams with backing indices across multiple data tiers.
  
  If set to 0 (default), the query does not terminate early.
  
  Default value is 0.
  
  timeout string
  
  The period of time to wait for a response from each shard. If no response is received before the timeout expires, the request fails and returns an error. Defaults to no timeout.
  
  track_scores boolean
  
  If true, calculate and return document scores, even if the scores are not used for sorting.
  
  Default value is false.
  
  version boolean
  
  If true, the request returns the document version as part of a hit.
  
  Default value is false.
  
  seq_no_primary_term boolean
  
  If true, the request returns sequence number and primary term of the last modification of each hit.
  
  External documentation
  
  stored_fields string | array[string]
  
  pit object
  
  Hide pit attributes Show pit attributes object
  
  id string Required
  
  keep_alive string
  
  A duration. Units can be nanos, micros, ms (milliseconds), s (seconds), m (minutes), h (hours) and d (days). Also accepts "0" without a unit and "-1" to indicate an unspecified value.
  
  runtime_mappings object
  
  stats array[string]
  
  The stats groups to associate with the search. Each group maintains a statistics aggregation for its associated searches. You can retrieve these stats using the indices stats API.
  
  id string
  
  params object
  
  Specifies any named parameters that are passed into the script as variables. Use parameters instead of hard-coded values to decrease compile time.
  
  Hide params attribute Show params attribute object
  
  * object Additional properties
  
  lang string
  
  Any of:
  string-1 string string-2 string
  
  Values are painless, expression, mustache, or java.
  
  options object
  
  Hide options attribute Show options attribute object
  
  * string Additional properties
  
  type string Required
  
  Values are boolean, composite, date, double, geo_point, geo_shape, ip, keyword, long, or lookup.
max_samples_per_key number

By default, the response of a sample query contains up to 10 samples, with one sample per unique set of join keys. Use the size parameter to get a smaller or larger set of samples. To retrieve more than one sample per set of join keys, use the max_samples_per_key parameter. Pipes are not supported for sample queries.

Default value is 1.

Responses

200 application/json
Hide response attributes Show response attributes object
- id string
- is_partial boolean
  
  If true, the response does not contain complete search results.
- is_running boolean
  
  If true, the search request is still executing.
- took number
  
  Time unit for milliseconds
- timed_out boolean
  
  If true, the request timed out before completion.
- hits object Required
  
  Hide hits attributes Show hits attributes object
  
  total object
  
  Hide total attributes Show total attributes object
  
  relation string Required
  
  Values are eq or gte.
  
  value number Required
  
  events array[object]
  
  Contains events matching the query. Each object represents a matching event.
  
  Hide events attributes Show events attributes object
  
  _index string Required
  
  _id string Required
  
  _source object Required
  
  Original JSON body passed for the event at index time.
  
  missing boolean
  
  Set to true for events in a timespan-constrained sequence that do not meet a given condition.
  
  fields object
  
  Hide fields attribute Show fields attribute object
  
  * array[object] Additional properties
  
  sequences array[object]
  
  Contains event sequences matching the query. Each object represents a matching sequence. This parameter is only returned for EQL queries containing a sequence.
  
  Hide sequences attributes Show sequences attributes object
  
  events array[object] Required
  
  Contains events matching the query. Each object represents a matching event.
  
  Hide events attributes Show events attributes object
  
  _index string Required
  
  _id string Required
  
  _source object Required
  
  Original JSON body passed for the event at index time.
  
  missing boolean
  
  Set to true for events in a timespan-constrained sequence that do not meet a given condition.
  
  fields object
  
  join_keys array[object]
  
  Shared field values used to constrain matches in the sequence. These are defined using the by keyword in the EQL query syntax.
- shard_failures array[object]
  
  Contains information about shard failures (if any), in case allow_partial_search_results=true
  
  Hide shard_failures attributes Show shard_failures attributes object
  
  index string
  
  node string
  
  reason object Required
  
  Cause and details about a request failure. This class defines the properties common to all error types. Additional details are also provided, that depend on the error type.
  
  Hide reason attributes Show reason attributes object
  
  type string Required
  
  The type of error
  
  reason string | null
  
  A human-readable explanation of the error, in English.
  
  One of:
  string-1 string string-2 string | null
  
  stack_trace string
  
  The server stack trace. Present only if the error_trace=true parameter was sent with the request.
  
  caused_by object
  
  Cause and details about a request failure. This class defines the properties common to all error types. Additional details are also provided, that depend on the error type.
  
  root_cause array[object]
  
  Cause and details about a request failure. This class defines the properties common to all error types. Additional details are also provided, that depend on the error type.
  
  Cause and details about a request failure. This class defines the properties common to all error types. Additional details are also provided, that depend on the error type.
  
  suppressed array[object]
  
  Cause and details about a request failure. This class defines the properties common to all error types. Additional details are also provided, that depend on the error type.
  
  Cause and details about a request failure. This class defines the properties common to all error types. Additional details are also provided, that depend on the error type.
  
  shard number Required
  
  status string

POST /{index}/_eql/search

GET /my-data-stream/_eql/search
{
  "query": """
    process where (process.name == "cmd.exe" and process.pid != 2013)
  """
}

resp = client.eql.search(
    index="my-data-stream",
    query="\n    process where (process.name == \"cmd.exe\" and process.pid != 2013)\n  ",
)

const response = await client.eql.search({
  index: "my-data-stream",
  query:
    '\n    process where (process.name == "cmd.exe" and process.pid != 2013)\n  ',
});

response = client.eql.search(
  index: "my-data-stream",
  body: {
    "query": "\n    process where (process.name == \"cmd.exe\" and process.pid != 2013)\n  "
  }
)

$resp = $client->eql()->search([
    "index" => "my-data-stream",
    "body" => [
        "query" => "\n    process where (process.name == \"cmd.exe\" and process.pid != 2013)\n  ",
    ],
]);

curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"query":"\n    process where (process.name == \"cmd.exe\" and process.pid != 2013)\n  "}' "$ELASTICSEARCH_URL/my-data-stream/_eql/search"

client.eql().search(s -> s
    .index("my-data-stream")
    .query(" process where (process.name == \"cmd.exe\" and process.pid != 2013) ")
);

Request examples

Run `GET /my-data-stream/_eql/search` to search for events that have a `process.name` of `cmd.exe` and a `process.pid` other than `2013`.

{
  "query": """
    process where (process.name == "cmd.exe" and process.pid != 2013)
  """
}

Run `GET /my-data-stream/_eql/search` to search for a sequence of events. The sequence starts with an event with an `event.category` of `file`, a `file.name` of `cmd.exe`, and a `process.pid` other than `2013`. It is followed by an event with an `event.category` of `process` and a `process.executable` that contains the substring `regsvr32`. These events must also share the same `process.pid` value.

{
  "query": """
    sequence by process.pid
      [ file where file.name == "cmd.exe" and process.pid != 2013 ]
      [ process where stringContains(process.executable, "regsvr32") ]
  """
}

Response examples (200)

{
  "is_partial": false,
  "is_running": false,
  "took": 6,
  "timed_out": false,
  "hits": {
    "total": {
      "value": 1,
      "relation": "eq"
    },
    "sequences": [
      {
        "join_keys": [
          2012
        ],
        "events": [
          {
            "_index": ".ds-my-data-stream-2099.12.07-000001",
            "_id": "AtOJ4UjUBAAx3XR5kcCM",
            "_source": {
              "@timestamp": "2099-12-06T11:04:07.000Z",
              "event": {
                "category": "file",
                "id": "dGCHwoeS",
                "sequence": 2
              },
              "file": {
                "accessed": "2099-12-07T11:07:08.000Z",
                "name": "cmd.exe",
                "path": "C:\\Windows\\System32\\cmd.exe",
                "type": "file",
                "size": 16384
              },
              "process": {
                "pid": 2012,
                "name": "cmd.exe",
                "executable": "C:\\Windows\\System32\\cmd.exe"
              }
            }
          },
          {
            "_index": ".ds-my-data-stream-2099.12.07-000001",
            "_id": "OQmfCaduce8zoHT93o4H",
            "_source": {
              "@timestamp": "2099-12-07T11:07:09.000Z",
              "event": {
                "category": "process",
                "id": "aR3NWVOs",
                "sequence": 4
              },
              "process": {
                "pid": 2012,
                "name": "regsvr32.exe",
                "command_line": "regsvr32.exe  /s /u /i:https://...RegSvr32.sct scrobj.dll",
                "executable": "C:\\Windows\\System32\\regsvr32.exe"
              }
            }
          }
        ]
      }
    ]
  }
}

Delete indices Generally available

DELETE /{index}

Api key auth

Deleting an index deletes its documents, shards, and metadata. It does not delete related Kibana components, such as data views, visualizations, or dashboards.

You cannot delete the current write index of a data stream. To delete the index, you must roll over the data stream so a new write index is created. You can then use the delete index API to delete the previous write index.

Required authorization

Index privileges: delete_index

Path parameters

index string | array[string] Required

Comma-separated list of indices to delete. You cannot specify index aliases. By default, this parameter does not support wildcards (*) or _all. To use wildcards or _all, set the action.destructive_requires_name cluster setting to false.

Query parameters

allow_no_indices boolean

If false, the request returns an error if any wildcard expression, index alias, or _all value targets only missing or closed indices. This behavior applies even if the request targets other open indices.
expand_wildcards string | array[string]
Type of index that wildcard patterns can match. If the request can target data streams, this argument determines whether wildcard expressions match hidden data streams. Supports comma-separated values, such as open,hidden.

Supported values include:
- all: Match any data stream or index, including hidden ones.
- open: Match open, non-hidden indices. Also matches any non-hidden data stream.
- closed: Match closed, non-hidden indices. Also matches any non-hidden data stream. Data streams cannot be closed.
- hidden: Match hidden data streams and hidden indices. Must be combined with open, closed, or both.
- none: Wildcard expressions are not accepted.
Values are all, open, closed, hidden, or none.
ignore_unavailable boolean

If false, the request returns an error if it targets a missing or closed index.
master_timeout string

Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.

Values are -1 or 0.
timeout string

Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.

Values are -1 or 0.

Responses

200 application/json
Hide response attributes Show response attributes object
- acknowledged boolean Required
  
  For a successful response, this value is always true. On failure, an exception is returned instead.
- _shards object
  
  Hide _shards attributes Show _shards attributes object
  
  failed number Required
  
  successful number Required
  
  total number Required
  
  failures array[object]
  
  Hide failures attributes Show failures attributes object
  
  index string
  
  node string
  
  reason object Required
  
  Cause and details about a request failure. This class defines the properties common to all error types. Additional details are also provided, that depend on the error type.
  
  Hide reason attributes Show reason attributes object
  
  type string Required
  
  The type of error
  
  reason
  
  stack_trace string
  
  The server stack trace. Present only if the error_trace=true parameter was sent with the request.
  
  caused_by object
  
  Cause and details about a request failure. This class defines the properties common to all error types. Additional details are also provided, that depend on the error type.
  
  root_cause array[object]
  
  suppressed array[object]
  
  shard number Required
  
  status string
  
  skipped number

DELETE /{index}

DELETE /books

resp = client.indices.delete(
    index="books",
)

const response = await client.indices.delete({
  index: "books",
});

response = client.indices.delete(
  index: "books"
)

$resp = $client->indices()->delete([
    "index" => "books",
]);

curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/books"

client.indices().delete(d -> d
    .index("books")
);

Check indices Generally available

HEAD /{index}

Api key auth

Check if one or more indices, index aliases, or data streams exist.

Path parameters

index string | array[string] Required

Comma-separated list of data streams, indices, and aliases. Supports wildcards (*).

Query parameters

allow_no_indices boolean

If false, the request returns an error if any wildcard expression, index alias, or _all value targets only missing or closed indices. This behavior applies even if the request targets other open indices.
expand_wildcards string | array[string]
Type of index that wildcard patterns can match. If the request can target data streams, this argument determines whether wildcard expressions match hidden data streams. Supports comma-separated values, such as open,hidden.

Supported values include:
- all: Match any data stream or index, including hidden ones.
- open: Match open, non-hidden indices. Also matches any non-hidden data stream.
- closed: Match closed, non-hidden indices. Also matches any non-hidden data stream. Data streams cannot be closed.
- hidden: Match hidden data streams and hidden indices. Must be combined with open, closed, or both.
- none: Wildcard expressions are not accepted.
Values are all, open, closed, hidden, or none.
flat_settings boolean

If true, returns settings in flat format.
ignore_unavailable boolean

If false, the request returns an error if it targets a missing or closed index.
include_defaults boolean

If true, return all default settings in the response.
local boolean

If true, the request retrieves information from the local node only.

Responses

200 application/json

HEAD /{index}

HEAD my-data-stream

resp = client.indices.exists(
    index="my-data-stream",
)

const response = await client.indices.exists({
  index: "my-data-stream",
});

response = client.indices.exists(
  index: "my-data-stream"
)

$resp = $client->indices()->exists([
    "index" => "my-data-stream",
]);

curl --head -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/my-data-stream"

client.indices().exists(e -> e
    .index("my-data-stream")
);

Create or update an alias Generally available

POST /{index}/_aliases/{name}

Api key auth

All methods and paths for this operation:

PUT /{index}/_alias/{name}

POST /{index}/_alias/{name}

PUT /{index}/_aliases/{name}

POST /{index}/_aliases/{name}

Adds a data stream or index to an alias.

Path parameters

index string | array[string] Required

Comma-separated list of data streams or indices to add. Supports wildcards (*). Wildcard patterns that match both data streams and indices return an error.
name string Required

Alias to update. If the alias doesn’t exist, the request creates it. Index alias names support date math.

Query parameters

master_timeout string

Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.

Values are -1 or 0.
timeout string

Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.

Values are -1 or 0.

application/json

Body

filter object

An Elasticsearch Query DSL (Domain Specific Language) object that defines a query.

External documentation
index_routing string
is_write_index boolean

If true, sets the write index or data stream for the alias. If an alias points to multiple indices or data streams and is_write_index isn’t set, the alias rejects write requests. If an index alias points to one index and is_write_index isn’t set, the index automatically acts as the write index. Data stream aliases don’t automatically set a write data stream, even if the alias points to one data stream.
routing string
search_routing string

Responses

200 application/json
Hide response attribute Show response attribute object
- acknowledged boolean Required
  
  For a successful response, this value is always true. On failure, an exception is returned instead.

POST /{index}/_aliases/{name}

POST _aliases
{
  "actions": [
    {
      "add": {
        "index": "my-data-stream",
        "alias": "my-alias"
      }
    }
  ]
}

resp = client.indices.update_aliases(
    actions=[
        {
            "add": {
                "index": "my-data-stream",
                "alias": "my-alias"
            }
        }
    ],
)

const response = await client.indices.updateAliases({
  actions: [
    {
      add: {
        index: "my-data-stream",
        alias: "my-alias",
      },
    },
  ],
});

response = client.indices.update_aliases(
  body: {
    "actions": [
      {
        "add": {
          "index": "my-data-stream",
          "alias": "my-alias"
        }
      }
    ]
  }
)

$resp = $client->indices()->updateAliases([
    "body" => [
        "actions" => array(
            [
                "add" => [
                    "index" => "my-data-stream",
                    "alias" => "my-alias",
                ],
            ],
        ),
    ],
]);

curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"actions":[{"add":{"index":"my-data-stream","alias":"my-alias"}}]}' "$ELASTICSEARCH_URL/_aliases"

client.indices().updateAliases(u -> u
    .actions(a -> a
        .add(ad -> ad
            .alias("my-alias")
            .index("my-data-stream")
        )
    )
);

Request example

{
  "actions": [
    {
      "add": {
        "index": "my-data-stream",
        "alias": "my-alias"
      }
    }
  ]
}

Get aliases Generally available

GET /{index}/_alias/{name}

Api key auth

All methods and paths for this operation:

GET /_alias

GET /_alias/{name}

GET /{index}/_alias

GET /{index}/_alias/{name}

Retrieves information for one or more data stream or index aliases.

Required authorization

Index privileges: view_index_metadata

Path parameters

index string | array[string] Required

Comma-separated list of data streams or indices used to limit the request. Supports wildcards (*). To target all data streams and indices, omit this parameter or use * or _all.
name string | array[string] Required

Comma-separated list of aliases to retrieve. Supports wildcards (*). To retrieve all aliases, omit this parameter or use * or _all.

Query parameters

allow_no_indices boolean

If false, the request returns an error if any wildcard expression, index alias, or _all value targets only missing or closed indices. This behavior applies even if the request targets other open indices.
expand_wildcards string | array[string]
Type of index that wildcard patterns can match. If the request can target data streams, this argument determines whether wildcard expressions match hidden data streams. Supports comma-separated values, such as open,hidden.

Supported values include:
- all: Match any data stream or index, including hidden ones.
- open: Match open, non-hidden indices. Also matches any non-hidden data stream.
- closed: Match closed, non-hidden indices. Also matches any non-hidden data stream. Data streams cannot be closed.
- hidden: Match hidden data streams and hidden indices. Must be combined with open, closed, or both.
- none: Wildcard expressions are not accepted.
Values are all, open, closed, hidden, or none.
ignore_unavailable boolean

If false, the request returns an error if it targets a missing or closed index.
master_timeout string

Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.

Values are -1 or 0.

Responses

200 application/json
Hide response attribute Show response attribute object
- * object Additional properties
  
  Hide * attribute Show * attribute object
  
  aliases object Required
  
  Hide aliases attribute Show aliases attribute object
  
  * object Additional properties
  
  Hide * attributes Show * attributes object
  
  filter object
  
  An Elasticsearch Query DSL (Domain Specific Language) object that defines a query.
  
  External documentation
  
  index_routing string
  
  Value used to route indexing operations to a specific shard. If specified, this overwrites the routing value for indexing operations.
  
  is_write_index boolean
  
  If true, the index is the write index for the alias.
  
  Default value is false.
  
  routing string
  
  Value used to route indexing and search operations to a specific shard.
  
  search_routing string
  
  Value used to route search operations to a specific shard. If specified, this overwrites the routing value for search operations.
  
  is_hidden boolean Generally available
  
  If true, the alias is hidden. All indices for the alias must have the same is_hidden value.
  
  Default value is false.

GET /{index}/_alias/{name}

GET _alias

resp = client.indices.get_alias()

const response = await client.indices.getAlias();

response = client.indices.get_alias

$resp = $client->indices()->getAlias();

curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_alias"

client.indices().getAlias(g -> g);

Check aliases Generally available

HEAD /{index}/_alias/{name}

Api key auth

All methods and paths for this operation:

HEAD /_alias/{name}

HEAD /{index}/_alias/{name}

Check if one or more data stream or index aliases exist.

Path parameters

index string | array[string] Required

Comma-separated list of data streams or indices used to limit the request. Supports wildcards (*). To target all data streams and indices, omit this parameter or use * or _all.
name string | array[string] Required

Comma-separated list of aliases to check. Supports wildcards (*).

Query parameters

allow_no_indices boolean

If false, the request returns an error if any wildcard expression, index alias, or _all value targets only missing or closed indices. This behavior applies even if the request targets other open indices.
expand_wildcards string | array[string]
Type of index that wildcard patterns can match. If the request can target data streams, this argument determines whether wildcard expressions match hidden data streams. Supports comma-separated values, such as open,hidden.

Supported values include:
- all: Match any data stream or index, including hidden ones.
- open: Match open, non-hidden indices. Also matches any non-hidden data stream.
- closed: Match closed, non-hidden indices. Also matches any non-hidden data stream. Data streams cannot be closed.
- hidden: Match hidden data streams and hidden indices. Must be combined with open, closed, or both.
- none: Wildcard expressions are not accepted.
Values are all, open, closed, hidden, or none.
ignore_unavailable boolean

If false, requests that include a missing data stream or index in the target indices or data streams return an error.
master_timeout string

Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.

Values are -1 or 0.

Responses

200 application/json

HEAD /{index}/_alias/{name}

HEAD _alias/my-alias

resp = client.indices.exists_alias(
    name="my-alias",
)

const response = await client.indices.existsAlias({
  name: "my-alias",
});

response = client.indices.exists_alias(
  name: "my-alias"
)

$resp = $client->indices()->existsAlias([
    "name" => "my-alias",
]);

curl --head -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_alias/my-alias"

client.indices().existsAlias(e -> e
    .name("my-alias")
);

Get mapping definitions Generally available

GET /{index}/_mapping

Api key auth

All methods and paths for this operation:

GET /_mapping

GET /{index}/_mapping

For data streams, the API retrieves mappings for the stream’s backing indices.

Required authorization

Index privileges: view_index_metadata

Path parameters

index string | array[string] Required

Comma-separated list of data streams, indices, and aliases used to limit the request. Supports wildcards (*). To target all data streams and indices, omit this parameter or use * or _all.

Query parameters

allow_no_indices boolean

If false, the request returns an error if any wildcard expression, index alias, or _all value targets only missing or closed indices. This behavior applies even if the request targets other open indices.
expand_wildcards string | array[string]
Type of index that wildcard patterns can match. If the request can target data streams, this argument determines whether wildcard expressions match hidden data streams. Supports comma-separated values, such as open,hidden.

Supported values include:
- all: Match any data stream or index, including hidden ones.
- open: Match open, non-hidden indices. Also matches any non-hidden data stream.
- closed: Match closed, non-hidden indices. Also matches any non-hidden data stream. Data streams cannot be closed.
- hidden: Match hidden data streams and hidden indices. Must be combined with open, closed, or both.
- none: Wildcard expressions are not accepted.
Values are all, open, closed, hidden, or none.
ignore_unavailable boolean

If false, the request returns an error if it targets a missing or closed index.
local boolean Deprecated

If true, the request retrieves information from the local node only.
master_timeout string

Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.

Values are -1 or 0.

Responses

200 application/json
Hide response attribute Show response attribute object
- * object Additional properties
  
  Hide * attributes Show * attributes object
  
  item object
  
  Hide item attributes Show item attributes object
  
  all_field object
  
  Hide all_field attributes Show all_field attributes object
  
  analyzer string Required
  
  enabled boolean Required
  
  omit_norms boolean Required
  
  search_analyzer string Required
  
  similarity string Required
  
  store boolean Required
  
  store_term_vector_offsets boolean Required
  
  store_term_vector_payloads boolean Required
  
  store_term_vector_positions boolean Required
  
  store_term_vectors boolean Required
  
  date_detection boolean
  
  dynamic string
  
  Values are strict, runtime, true, or false.
  
  dynamic_date_formats array[string]
  
  dynamic_templates array[object]
  
  _field_names object
  
  Hide _field_names attribute Show _field_names attribute object
  
  enabled boolean Required
  
  index_field object
  
  Hide index_field attribute Show index_field attribute object
  
  enabled boolean Required
  
  _meta object
  
  Hide _meta attribute Show _meta attribute object
  
  * object Additional properties
  
  numeric_detection boolean
  
  properties object
  
  _routing object
  
  Hide _routing attribute Show _routing attribute object
  
  required boolean Required
  
  _size object
  
  Hide _size attribute Show _size attribute object
  
  enabled boolean Required
  
  _source object
  
  Hide _source attributes Show _source attributes object
  
  compress boolean
  
  compress_threshold string
  
  enabled boolean
  
  excludes array[string]
  
  includes array[string]
  
  mode string
  
  Values are disabled, stored, or synthetic.
  
  runtime object
  
  Hide runtime attribute Show runtime attribute object
  
  * object Additional properties
  
  Hide * attributes Show * attributes object
  
  fields object
  
  For type composite
  
  Hide fields attribute Show fields attribute object
  
  * object Additional properties
  
  fetch_fields array[object]
  
  For type lookup
  
  format string
  
  A custom format for date type runtime fields.
  
  input_field string
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  target_field string
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  target_index string
  
  script object
  
  Hide script attributes Show script attributes object
  
  source
  
  id string
  
  params object
  
  Specifies any named parameters that are passed into the script as variables. Use parameters instead of hard-coded values to decrease compile time.
  
  lang
  
  options object
  
  type string Required
  
  Values are boolean, composite, date, double, geo_point, geo_shape, ip, keyword, long, or lookup.
  
  enabled boolean
  
  subobjects string
  
  Values are true or false.
  
  _data_stream_timestamp object
  
  Hide _data_stream_timestamp attribute Show _data_stream_timestamp attribute object
  
  enabled boolean Required
  
  mappings object Required
  
  Hide mappings attributes Show mappings attributes object
  
  all_field object
  
  Hide all_field attributes Show all_field attributes object
  
  analyzer string Required
  
  enabled boolean Required
  
  omit_norms boolean Required
  
  search_analyzer string Required
  
  similarity string Required
  
  store boolean Required
  
  store_term_vector_offsets boolean Required
  
  store_term_vector_payloads boolean Required
  
  store_term_vector_positions boolean Required
  
  store_term_vectors boolean Required
  
  date_detection boolean
  
  dynamic string
  
  Values are strict, runtime, true, or false.
  
  dynamic_date_formats array[string]
  
  dynamic_templates array[object]
  
  _field_names object
  
  Hide _field_names attribute Show _field_names attribute object
  
  enabled boolean Required
  
  index_field object
  
  Hide index_field attribute Show index_field attribute object
  
  enabled boolean Required
  
  _meta object
  
  Hide _meta attribute Show _meta attribute object
  
  * object Additional properties
  
  numeric_detection boolean
  
  properties object
  
  _routing object
  
  Hide _routing attribute Show _routing attribute object
  
  required boolean Required
  
  _size object
  
  Hide _size attribute Show _size attribute object
  
  enabled boolean Required
  
  _source object
  
  Hide _source attributes Show _source attributes object
  
  compress boolean
  
  compress_threshold string
  
  enabled boolean
  
  excludes array[string]
  
  includes array[string]
  
  mode string
  
  Values are disabled, stored, or synthetic.
  
  runtime object
  
  Hide runtime attribute Show runtime attribute object
  
  * object Additional properties
  
  Hide * attributes Show * attributes object
  
  fields object
  
  For type composite
  
  Hide fields attribute Show fields attribute object
  
  * object Additional properties
  
  fetch_fields array[object]
  
  For type lookup
  
  format string
  
  A custom format for date type runtime fields.
  
  input_field string
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  target_field string
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  target_index string
  
  script object
  
  Hide script attributes Show script attributes object
  
  source
  
  id string
  
  params object
  
  Specifies any named parameters that are passed into the script as variables. Use parameters instead of hard-coded values to decrease compile time.
  
  lang
  
  options object
  
  type string Required
  
  Values are boolean, composite, date, double, geo_point, geo_shape, ip, keyword, long, or lookup.
  
  enabled boolean
  
  subobjects string
  
  Values are true or false.
  
  _data_stream_timestamp object
  
  Hide _data_stream_timestamp attribute Show _data_stream_timestamp attribute object
  
  enabled boolean Required

GET /{index}/_mapping

GET /books/_mapping

resp = client.indices.get_mapping(
    index="books",
)

const response = await client.indices.getMapping({
  index: "books",
});

response = client.indices.get_mapping(
  index: "books"
)

$resp = $client->indices()->getMapping([
    "index" => "books",
]);

curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/books/_mapping"

client.indices().getMapping(g -> g
    .index("books")
);

Inference

Inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Azure, Google AI Studio or Hugging Face. For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models. However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.

Create an inference endpoint Generally available

PUT /_inference/{task_type}/{inference_id}

Api key auth

All methods and paths for this operation:

PUT /_inference/{inference_id}

PUT /_inference/{task_type}/{inference_id}

IMPORTANT: The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Mistral, Azure OpenAI, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face. For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models. However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.

The following integrations are available through the inference API. You can find the available task types next to the integration name:

AlibabaCloud AI Search (completion, rerank, sparse_embedding, text_embedding)
Amazon Bedrock (completion, text_embedding)
Anthropic (completion)
Azure AI Studio (completion, 'rerank', text_embedding)
Azure OpenAI (completion, text_embedding)
Cohere (completion, rerank, text_embedding)
DeepSeek (completion, chat_completion)
Elasticsearch (rerank, sparse_embedding, text_embedding - this service is for built-in models and models uploaded through Eland)
ELSER (sparse_embedding)
Google AI Studio (completion, text_embedding)
Google Vertex AI (rerank, text_embedding)
Hugging Face (chat_completion, completion, rerank, text_embedding)
Mistral (chat_completion, completion, text_embedding)
OpenAI (chat_completion, completion, text_embedding)
VoyageAI (text_embedding, rerank)
Watsonx inference integration (text_embedding)
JinaAI (text_embedding, rerank)

Required authorization

Cluster privileges: manage_inference

Path parameters

task_type string Required

The task type. Refer to the integration list in the API description for the available task types.

Values are sparse_embedding, text_embedding, rerank, completion, or chat_completion.
inference_id string Required

The inference Id

Query parameters

timeout string

Specifies the amount of time to wait for the inference endpoint to be created.

Values are -1 or 0.

application/json

Body Required

chunking_settings object

Chunking configuration object
Hide chunking_settings attributes Show chunking_settings attributes object
- max_chunk_size number
  
  The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).
  
  Default value is 250.
- overlap number
  
  The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.
  
  Default value is 100.
- sentence_overlap number
  
  The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.
  
  Default value is 1.
- separator_group string Required
  
  This parameter is only applicable when using the recursive chunking strategy.
  
  Sets a predefined list of separators in the saved chunking settings based on the selected text type. Values can be markdown or plaintext.
  
  Using this parameter is an alternative to manually specifying a custom separators list.
- separators array[string] Required
  
  A list of strings used as possible split points when chunking text with the recursive strategy.
  
  Each string can be a plain string or a regular expression (regex) pattern. The system tries each separator in order to split the text, starting from the first item in the list.
  
  After splitting, it attempts to recombine smaller pieces into larger chunks that stay within the max_chunk_size limit, to reduce the total number of chunks generated.
- strategy string
  The chunking strategy: sentence, word, none or recursive.
  
  If strategy is set to recursive, you must also specify:
  
  max_chunk_size
  
  either separators orseparator_group
  
  Learn more about different chunking strategies in the linked documentation.
  Default value is sentence.
  
  External documentation
service string Required

The service type
service_settings object Required
task_settings object

Responses

200 application/json
Hide response attributes Show response attributes object
Represents an inference endpoint as returned by the GET API
- chunking_settings object
  
  Chunking configuration object
  
  Hide chunking_settings attributes Show chunking_settings attributes object
  
  max_chunk_size number
  
  The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).
  
  Default value is 250.
  
  overlap number
  
  The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.
  
  Default value is 100.
  
  sentence_overlap number
  
  The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.
  
  Default value is 1.
  
  separator_group string Required
  
  This parameter is only applicable when using the recursive chunking strategy.
  
  Sets a predefined list of separators in the saved chunking settings based on the selected text type. Values can be markdown or plaintext.
  
  Using this parameter is an alternative to manually specifying a custom separators list.
  
  separators array[string] Required
  
  A list of strings used as possible split points when chunking text with the recursive strategy.
  
  Each string can be a plain string or a regular expression (regex) pattern. The system tries each separator in order to split the text, starting from the first item in the list.
  
  After splitting, it attempts to recombine smaller pieces into larger chunks that stay within the max_chunk_size limit, to reduce the total number of chunks generated.
  
  strategy string
  
  The chunking strategy: sentence, word, none or recursive.
  
  If strategy is set to recursive, you must also specify:
  
  max_chunk_size
  
  either separators orseparator_group
  
  Learn more about different chunking strategies in the linked documentation.
  
  Default value is sentence.
  
  External documentation
- service string Required
  
  The service type
- service_settings object Required
- task_settings object
- inference_id string Required
  
  The inference Id
- task_type string Required
  
  Values are sparse_embedding, text_embedding, rerank, completion, or chat_completion.

PUT /_inference/{task_type}/{inference_id}

PUT _inference/rerank/my-rerank-model
{
 "service": "cohere",
 "service_settings": {
   "model_id": "rerank-english-v3.0",
   "api_key": "{{COHERE_API_KEY}}"
 }
 "chunking_settings": {
   "strategy": "recursive",
   "max_chunk_size": 200,
   "separator_group": "markdown"
}

resp = client.inference.put(
    task_type="rerank",
    inference_id="my-rerank-model",
    inference_config={
        "service": "cohere",
        "service_settings": {
            "model_id": "rerank-english-v3.0",
            "api_key": "{{COHERE_API_KEY}}"
        }
    },
)

const response = await client.inference.put({
  task_type: "rerank",
  inference_id: "my-rerank-model",
  inference_config: {
    service: "cohere",
    service_settings: {
      model_id: "rerank-english-v3.0",
      api_key: "{{COHERE_API_KEY}}",
    },
  },
});

response = client.inference.put(
  task_type: "rerank",
  inference_id: "my-rerank-model",
  body: {
    "service": "cohere",
    "service_settings": {
      "model_id": "rerank-english-v3.0",
      "api_key": "{{COHERE_API_KEY}}"
    }
  }
)

$resp = $client->inference()->put([
    "task_type" => "rerank",
    "inference_id" => "my-rerank-model",
    "body" => [
        "service" => "cohere",
        "service_settings" => [
            "model_id" => "rerank-english-v3.0",
            "api_key" => "{{COHERE_API_KEY}}",
        ],
    ],
]);

curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"cohere","service_settings":{"model_id":"rerank-english-v3.0","api_key":"{{COHERE_API_KEY}}"}}' "$ELASTICSEARCH_URL/_inference/rerank/my-rerank-model"

client.inference().put(p -> p
    .inferenceId("my-rerank-model")
    .taskType(TaskType.Rerank)
    .inferenceConfig(i -> i
        .service("cohere")
        .serviceSettings(JsonData.fromJson("{\"model_id\":\"rerank-english-v3.0\",\"api_key\":\"{{COHERE_API_KEY}}\"}"))
    )
);

Request example

An example body for a `PUT _inference/rerank/my-rerank-model` request.

{
 "service": "cohere",
 "service_settings": {
   "model_id": "rerank-english-v3.0",
   "api_key": "{{COHERE_API_KEY}}"
 }
 "chunking_settings": {
   "strategy": "recursive",
   "max_chunk_size": 200,
   "separator_group": "markdown"
}

Create an Azure OpenAI inference endpoint Generally available

PUT /_inference/{task_type}/{azureopenai_inference_id}

Api key auth

Create an inference endpoint to perform an inference task with the azureopenai service.

The list of chat completion models that you can choose from in your Azure OpenAI deployment include:

The list of embeddings models that you can choose from in your deployment can be found in the Azure models documentation.

Required authorization

Cluster privileges: manage_inference

Path parameters

task_type string

The type of the inference task that the model will perform. NOTE: The chat_completion task type only supports streaming and only through the _stream API.

Values are completion or text_embedding.
azureopenai_inference_id string Required

The unique identifier of the inference endpoint.

Query parameters

timeout string

Specifies the amount of time to wait for the inference endpoint to be created.

Values are -1 or 0.

application/json

Body

chunking_settings object

Chunking configuration object
Hide chunking_settings attributes Show chunking_settings attributes object
- max_chunk_size number
  
  The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).
  
  Default value is 250.
- overlap number
  
  The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.
  
  Default value is 100.
- sentence_overlap number
  
  The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.
  
  Default value is 1.
- separator_group string Required
  
  This parameter is only applicable when using the recursive chunking strategy.
  
  Sets a predefined list of separators in the saved chunking settings based on the selected text type. Values can be markdown or plaintext.
  
  Using this parameter is an alternative to manually specifying a custom separators list.
- separators array[string] Required
  
  A list of strings used as possible split points when chunking text with the recursive strategy.
  
  Each string can be a plain string or a regular expression (regex) pattern. The system tries each separator in order to split the text, starting from the first item in the list.
  
  After splitting, it attempts to recombine smaller pieces into larger chunks that stay within the max_chunk_size limit, to reduce the total number of chunks generated.
- strategy string
  The chunking strategy: sentence, word, none or recursive.
  
  If strategy is set to recursive, you must also specify:
  
  max_chunk_size
  
  either separators orseparator_group
  
  Learn more about different chunking strategies in the linked documentation.
  Default value is sentence.
  
  External documentation
service string Required

Value is azureopenai.
service_settings object Required
Hide service_settings attributes Show service_settings attributes object
- api_key string
  
  A valid API key for your Azure OpenAI account. You must specify either api_key or entra_id. If you do not provide either or you provide both, you will receive an error when you try to create your model.
  
  IMPORTANT: You need to provide the API key only once, during the inference model creation. The get inference endpoint API does not retrieve your API key. After creating the inference model, you cannot change the associated API key. If you want to use a different API key, delete the inference model and recreate it with the same name and the updated API key.
  
  External documentation
- api_version string Required
  
  The Azure API version ID to use. It is recommended to use the latest supported non-preview version.
- deployment_id string Required
  
  The deployment name of your deployed models. Your Azure OpenAI deployments can be found though the Azure OpenAI Studio portal that is linked to your subscription.
  
  External documentation
- entra_id string
  
  A valid Microsoft Entra token. You must specify either api_key or entra_id. If you do not provide either or you provide both, you will receive an error when you try to create your model.
  
  External documentation
- rate_limit object
  
  This setting helps to minimize the number of rate limit errors returned from the service.
  Hide rate_limit attribute Show rate_limit attribute object
  
  requests_per_minute number
  
  The number of requests allowed per minute. By default, the number of requests allowed per minute is set by each service as follows:
  
  alibabacloud-ai-search service: 1000
  
  anthropic service: 50
  
  azureaistudio service: 240
  
  azureopenai service and task type text_embedding: 1440
  
  azureopenai service and task type completion: 120
  
  cohere service: 10000
  
  elastic service and task type chat_completion: 240
  
  googleaistudio service: 360
  
  googlevertexai service: 30000
  
  hugging_face service: 3000
  
  jinaai service: 2000
  
  mistral service: 240
  
  openai service and task type text_embedding: 3000
  
  openai service and task type completion: 500
  
  voyageai service: 2000
  
  watsonxai service: 120
- resource_name string Required
  
  The name of your Azure OpenAI resource. You can find this from the list of resources in the Azure Portal for your subscription.
  
  External documentation
task_settings object
Hide task_settings attribute Show task_settings attribute object
- user string
  
  For a completion or text_embedding task, specify the user issuing the request. This information can be used for abuse detection.

Responses

200 application/json
Hide response attributes Show response attributes object
- chunking_settings object
  
  Chunking configuration object
  
  Hide chunking_settings attributes Show chunking_settings attributes object
  
  max_chunk_size number
  
  The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).
  
  Default value is 250.
  
  overlap number
  
  The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.
  
  Default value is 100.
  
  sentence_overlap number
  
  The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.
  
  Default value is 1.
  
  separator_group string Required
  
  This parameter is only applicable when using the recursive chunking strategy.
  
  Sets a predefined list of separators in the saved chunking settings based on the selected text type. Values can be markdown or plaintext.
  
  Using this parameter is an alternative to manually specifying a custom separators list.
  
  separators array[string] Required
  
  A list of strings used as possible split points when chunking text with the recursive strategy.
  
  Each string can be a plain string or a regular expression (regex) pattern. The system tries each separator in order to split the text, starting from the first item in the list.
  
  After splitting, it attempts to recombine smaller pieces into larger chunks that stay within the max_chunk_size limit, to reduce the total number of chunks generated.
  
  strategy string
  
  The chunking strategy: sentence, word, none or recursive.
  
  If strategy is set to recursive, you must also specify:
  
  max_chunk_size
  
  either separators orseparator_group
  
  Learn more about different chunking strategies in the linked documentation.
  
  Default value is sentence.
  
  External documentation
- service string Required
  
  The service type
- service_settings object Required
- task_settings object
- inference_id string Required
  
  The inference Id
- task_type string Required
  
  Values are text_embedding or completion.

PUT /_inference/{task_type}/{azureopenai_inference_id}

PUT _inference/text_embedding/azure_openai_embeddings
{
    "service": "azureopenai",
    "service_settings": {
        "api_key": "Api-Key",
        "resource_name": "Resource-name",
        "deployment_id": "Deployment-id",
        "api_version": "2024-02-01"
    }
}

resp = client.inference.put(
    task_type="text_embedding",
    inference_id="azure_openai_embeddings",
    inference_config={
        "service": "azureopenai",
        "service_settings": {
            "api_key": "Api-Key",
            "resource_name": "Resource-name",
            "deployment_id": "Deployment-id",
            "api_version": "2024-02-01"
        }
    },
)

const response = await client.inference.put({
  task_type: "text_embedding",
  inference_id: "azure_openai_embeddings",
  inference_config: {
    service: "azureopenai",
    service_settings: {
      api_key: "Api-Key",
      resource_name: "Resource-name",
      deployment_id: "Deployment-id",
      api_version: "2024-02-01",
    },
  },
});

response = client.inference.put(
  task_type: "text_embedding",
  inference_id: "azure_openai_embeddings",
  body: {
    "service": "azureopenai",
    "service_settings": {
      "api_key": "Api-Key",
      "resource_name": "Resource-name",
      "deployment_id": "Deployment-id",
      "api_version": "2024-02-01"
    }
  }
)

$resp = $client->inference()->put([
    "task_type" => "text_embedding",
    "inference_id" => "azure_openai_embeddings",
    "body" => [
        "service" => "azureopenai",
        "service_settings" => [
            "api_key" => "Api-Key",
            "resource_name" => "Resource-name",
            "deployment_id" => "Deployment-id",
            "api_version" => "2024-02-01",
        ],
    ],
]);

curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"azureopenai","service_settings":{"api_key":"Api-Key","resource_name":"Resource-name","deployment_id":"Deployment-id","api_version":"2024-02-01"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/azure_openai_embeddings"

client.inference().put(p -> p
    .inferenceId("azure_openai_embeddings")
    .taskType(TaskType.TextEmbedding)
    .inferenceConfig(i -> i
        .service("azureopenai")
        .serviceSettings(JsonData.fromJson("{\"api_key\":\"Api-Key\",\"resource_name\":\"Resource-name\",\"deployment_id\":\"Deployment-id\",\"api_version\":\"2024-02-01\"}"))
    )
);

Request examples

Run `PUT _inference/text_embedding/azure_openai_embeddings` to create an inference endpoint that performs a `text_embedding` task. You do not specify a model, as it is defined already in the Azure OpenAI deployment.

{
    "service": "azureopenai",
    "service_settings": {
        "api_key": "Api-Key",
        "resource_name": "Resource-name",
        "deployment_id": "Deployment-id",
        "api_version": "2024-02-01"
    }
}

Run `PUT _inference/completion/azure_openai_completion` to create an inference endpoint that performs a `completion` task.

{
    "service": "azureopenai",
    "service_settings": {
        "api_key": "Api-Key",
        "resource_name": "Resource-name",
        "deployment_id": "Deployment-id",
        "api_version": "2024-02-01"
    }
}

Create an ELSER inference endpoint Deprecated Generally available

PUT /_inference/{task_type}/{elser_inference_id}

Api key auth

Create an inference endpoint to perform an inference task with the elser service. You can also deploy ELSER by using the Elasticsearch inference integration.

Your Elasticsearch deployment contains a preconfigured ELSER inference endpoint, you only need to create the enpoint using the API if you want to customize the settings.

The API request will automatically download and deploy the ELSER model if it isn't already downloaded.

You might see a 502 bad gateway error in the response when using the Kibana Console. This error usually just reflects a timeout, while the model downloads in the background. You can check the download progress in the Machine Learning UI. If using the Python client, you can set the timeout parameter to a higher value.

After creating the endpoint, wait for the model deployment to complete before using it. To verify the deployment status, use the get trained model statistics API. Look for "state": "fully_allocated" in the response and ensure that the "allocation_count" matches the "target_allocation_count". Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.

Required authorization

Cluster privileges: manage_inference

Path parameters

task_type string

The type of the inference task that the model will perform.

Value is sparse_embedding.
elser_inference_id string Required

The unique identifier of the inference endpoint.

Query parameters

timeout string

Specifies the amount of time to wait for the inference endpoint to be created.

Values are -1 or 0.

application/json

Body

chunking_settings object

Chunking configuration object
Hide chunking_settings attributes Show chunking_settings attributes object
- max_chunk_size number
  
  The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).
  
  Default value is 250.
- overlap number
  
  The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.
  
  Default value is 100.
- sentence_overlap number
  
  The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.
  
  Default value is 1.
- separator_group string Required
  
  This parameter is only applicable when using the recursive chunking strategy.
  
  Sets a predefined list of separators in the saved chunking settings based on the selected text type. Values can be markdown or plaintext.
  
  Using this parameter is an alternative to manually specifying a custom separators list.
- separators array[string] Required
  
  A list of strings used as possible split points when chunking text with the recursive strategy.
  
  Each string can be a plain string or a regular expression (regex) pattern. The system tries each separator in order to split the text, starting from the first item in the list.
  
  After splitting, it attempts to recombine smaller pieces into larger chunks that stay within the max_chunk_size limit, to reduce the total number of chunks generated.
- strategy string
  The chunking strategy: sentence, word, none or recursive.
  
  If strategy is set to recursive, you must also specify:
  
  max_chunk_size
  
  either separators orseparator_group
  
  Learn more about different chunking strategies in the linked documentation.
  Default value is sentence.
  
  External documentation
service string Required

Value is elser.
service_settings object Required
Hide service_settings attributes Show service_settings attributes object
- adaptive_allocations object
  Hide adaptive_allocations attributes Show adaptive_allocations attributes object
  
  enabled boolean
  
  Turn on adaptive_allocations.
  
  Default value is false.
  
  max_number_of_allocations number
  
  The maximum number of allocations to scale to. If set, it must be greater than or equal to min_number_of_allocations.
  
  min_number_of_allocations number
  
  The minimum number of allocations to scale to. If set, it must be greater than or equal to 0. If not defined, the deployment scales to 0.
- num_allocations number Required
  
  The total number of allocations this model is assigned across machine learning nodes. Increasing this value generally increases the throughput. If adaptive allocations is enabled, do not set this value because it's automatically set.
- num_threads number Required
  
  The number of threads used by each model allocation during inference. Increasing this value generally increases the speed per inference request. The inference process is a compute-bound process; threads_per_allocations must not exceed the number of available allocated processors per node. The value must be a power of 2. The maximum value is 32.
  
  If you want to optimize your ELSER endpoint for ingest, set the number of threads to 1. If you want to optimize your ELSER endpoint for search, set the number of threads to greater than 1.

Responses

200 application/json
Hide response attributes Show response attributes object
- chunking_settings object
  
  Chunking configuration object
  
  Hide chunking_settings attributes Show chunking_settings attributes object
  
  max_chunk_size number
  
  The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).
  
  Default value is 250.
  
  overlap number
  
  The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.
  
  Default value is 100.
  
  sentence_overlap number
  
  The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.
  
  Default value is 1.
  
  separator_group string Required
  
  This parameter is only applicable when using the recursive chunking strategy.
  
  Sets a predefined list of separators in the saved chunking settings based on the selected text type. Values can be markdown or plaintext.
  
  Using this parameter is an alternative to manually specifying a custom separators list.
  
  separators array[string] Required
  
  A list of strings used as possible split points when chunking text with the recursive strategy.
  
  Each string can be a plain string or a regular expression (regex) pattern. The system tries each separator in order to split the text, starting from the first item in the list.
  
  After splitting, it attempts to recombine smaller pieces into larger chunks that stay within the max_chunk_size limit, to reduce the total number of chunks generated.
  
  strategy string
  
  The chunking strategy: sentence, word, none or recursive.
  
  If strategy is set to recursive, you must also specify:
  
  max_chunk_size
  
  either separators orseparator_group
  
  Learn more about different chunking strategies in the linked documentation.
  
  Default value is sentence.
  
  External documentation
- service string Required
  
  The service type
- service_settings object Required
- task_settings object
- inference_id string Required
  
  The inference Id
- task_type string Required
  
  Value is sparse_embedding.

PUT /_inference/{task_type}/{elser_inference_id}

PUT _inference/sparse_embedding/my-elser-model
{
    "service": "elser",
    "service_settings": {
        "num_allocations": 1,
        "num_threads": 1
    }
}

resp = client.inference.put(
    task_type="sparse_embedding",
    inference_id="my-elser-model",
    inference_config={
        "service": "elser",
        "service_settings": {
            "num_allocations": 1,
            "num_threads": 1
        }
    },
)

const response = await client.inference.put({
  task_type: "sparse_embedding",
  inference_id: "my-elser-model",
  inference_config: {
    service: "elser",
    service_settings: {
      num_allocations: 1,
      num_threads: 1,
    },
  },
});

response = client.inference.put(
  task_type: "sparse_embedding",
  inference_id: "my-elser-model",
  body: {
    "service": "elser",
    "service_settings": {
      "num_allocations": 1,
      "num_threads": 1
    }
  }
)

$resp = $client->inference()->put([
    "task_type" => "sparse_embedding",
    "inference_id" => "my-elser-model",
    "body" => [
        "service" => "elser",
        "service_settings" => [
            "num_allocations" => 1,
            "num_threads" => 1,
        ],
    ],
]);

curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"elser","service_settings":{"num_allocations":1,"num_threads":1}}' "$ELASTICSEARCH_URL/_inference/sparse_embedding/my-elser-model"

client.inference().put(p -> p
    .inferenceId("my-elser-model")
    .taskType(TaskType.SparseEmbedding)
    .inferenceConfig(i -> i
        .service("elser")
        .serviceSettings(JsonData.fromJson("{\"num_allocations\":1,\"num_threads\":1}"))
    )
);

Request examples

Run `PUT _inference/sparse_embedding/my-elser-model` to create an inference endpoint that performs a `sparse_embedding` task. The request will automatically download the ELSER model if it isn't already downloaded and then deploy the model.

{
    "service": "elser",
    "service_settings": {
        "num_allocations": 1,
        "num_threads": 1
    }
}

Run `PUT _inference/sparse_embedding/my-elser-model` to create an inference endpoint that performs a `sparse_embedding` task with adaptive allocations. When adaptive allocations are enabled, the number of allocations of the model is set automatically based on the current load.

{
    "service": "elser",
    "service_settings": {
        "adaptive_allocations": {
            "enabled": true,
            "min_number_of_allocations": 3,
            "max_number_of_allocations": 10
        },
        "num_threads": 1
    }
}

Response examples (200)

A successful response when creating an ELSER inference endpoint.

{
  "inference_id": "my-elser-model",
  "task_type": "sparse_embedding",
  "service": "elser",
  "service_settings": {
    "num_allocations": 1,
    "num_threads": 1
  },
  "task_settings": {}
}

Create an Google AI Studio inference endpoint Generally available

PUT /_inference/{task_type}/{googleaistudio_inference_id}

Api key auth

Create an inference endpoint to perform an inference task with the googleaistudio service.

Required authorization

Cluster privileges: manage_inference

Path parameters

task_type string

The type of the inference task that the model will perform.

Values are completion or text_embedding.
googleaistudio_inference_id string Required

The unique identifier of the inference endpoint.

Query parameters

timeout string

Specifies the amount of time to wait for the inference endpoint to be created.

Values are -1 or 0.

application/json

Body

chunking_settings object

Chunking configuration object
Hide chunking_settings attributes Show chunking_settings attributes object
- max_chunk_size number
  
  The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).
  
  Default value is 250.
- overlap number
  
  The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.
  
  Default value is 100.
- sentence_overlap number
  
  The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.
  
  Default value is 1.
- separator_group string Required
  
  This parameter is only applicable when using the recursive chunking strategy.
  
  Sets a predefined list of separators in the saved chunking settings based on the selected text type. Values can be markdown or plaintext.
  
  Using this parameter is an alternative to manually specifying a custom separators list.
- separators array[string] Required
  
  A list of strings used as possible split points when chunking text with the recursive strategy.
  
  Each string can be a plain string or a regular expression (regex) pattern. The system tries each separator in order to split the text, starting from the first item in the list.
  
  After splitting, it attempts to recombine smaller pieces into larger chunks that stay within the max_chunk_size limit, to reduce the total number of chunks generated.
- strategy string
  The chunking strategy: sentence, word, none or recursive.
  
  If strategy is set to recursive, you must also specify:
  
  max_chunk_size
  
  either separators orseparator_group
  
  Learn more about different chunking strategies in the linked documentation.
  Default value is sentence.
  
  External documentation
service string Required

Value is googleaistudio.
service_settings object Required
Hide service_settings attributes Show service_settings attributes object
- api_key string Required
  
  A valid API key of your Google Gemini account.
- model_id string Required
  
  The name of the model to use for the inference task. Refer to the Google documentation for the list of supported models.
  
  External documentation
- rate_limit object
  
  This setting helps to minimize the number of rate limit errors returned from the service.
  Hide rate_limit attribute Show rate_limit attribute object
  
  requests_per_minute number
  
  The number of requests allowed per minute. By default, the number of requests allowed per minute is set by each service as follows:
  
  alibabacloud-ai-search service: 1000
  
  anthropic service: 50
  
  azureaistudio service: 240
  
  azureopenai service and task type text_embedding: 1440
  
  azureopenai service and task type completion: 120
  
  cohere service: 10000
  
  elastic service and task type chat_completion: 240
  
  googleaistudio service: 360
  
  googlevertexai service: 30000
  
  hugging_face service: 3000
  
  jinaai service: 2000
  
  mistral service: 240
  
  openai service and task type text_embedding: 3000
  
  openai service and task type completion: 500
  
  voyageai service: 2000
  
  watsonxai service: 120

Responses

200 application/json
Hide response attributes Show response attributes object
- chunking_settings object
  
  Chunking configuration object
  
  Hide chunking_settings attributes Show chunking_settings attributes object
  
  max_chunk_size number
  
  The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).
  
  Default value is 250.
  
  overlap number
  
  The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.
  
  Default value is 100.
  
  sentence_overlap number
  
  The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.
  
  Default value is 1.
  
  separator_group string Required
  
  This parameter is only applicable when using the recursive chunking strategy.
  
  Sets a predefined list of separators in the saved chunking settings based on the selected text type. Values can be markdown or plaintext.
  
  Using this parameter is an alternative to manually specifying a custom separators list.
  
  separators array[string] Required
  
  A list of strings used as possible split points when chunking text with the recursive strategy.
  
  Each string can be a plain string or a regular expression (regex) pattern. The system tries each separator in order to split the text, starting from the first item in the list.
  
  After splitting, it attempts to recombine smaller pieces into larger chunks that stay within the max_chunk_size limit, to reduce the total number of chunks generated.
  
  strategy string
  
  The chunking strategy: sentence, word, none or recursive.
  
  If strategy is set to recursive, you must also specify:
  
  max_chunk_size
  
  either separators orseparator_group
  
  Learn more about different chunking strategies in the linked documentation.
  
  Default value is sentence.
  
  External documentation
- service string Required
  
  The service type
- service_settings object Required
- task_settings object
- inference_id string Required
  
  The inference Id
- task_type string Required
  
  Values are text_embedding or completion.

PUT /_inference/{task_type}/{googleaistudio_inference_id}

PUT _inference/completion/google_ai_studio_completion
{
    "service": "googleaistudio",
    "service_settings": {
        "api_key": "api-key",
        "model_id": "model-id"
    }
}

resp = client.inference.put(
    task_type="completion",
    inference_id="google_ai_studio_completion",
    inference_config={
        "service": "googleaistudio",
        "service_settings": {
            "api_key": "api-key",
            "model_id": "model-id"
        }
    },
)

const response = await client.inference.put({
  task_type: "completion",
  inference_id: "google_ai_studio_completion",
  inference_config: {
    service: "googleaistudio",
    service_settings: {
      api_key: "api-key",
      model_id: "model-id",
    },
  },
});

response = client.inference.put(
  task_type: "completion",
  inference_id: "google_ai_studio_completion",
  body: {
    "service": "googleaistudio",
    "service_settings": {
      "api_key": "api-key",
      "model_id": "model-id"
    }
  }
)

$resp = $client->inference()->put([
    "task_type" => "completion",
    "inference_id" => "google_ai_studio_completion",
    "body" => [
        "service" => "googleaistudio",
        "service_settings" => [
            "api_key" => "api-key",
            "model_id" => "model-id",
        ],
    ],
]);

curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"googleaistudio","service_settings":{"api_key":"api-key","model_id":"model-id"}}' "$ELASTICSEARCH_URL/_inference/completion/google_ai_studio_completion"

client.inference().put(p -> p
    .inferenceId("google_ai_studio_completion")
    .taskType(TaskType.Completion)
    .inferenceConfig(i -> i
        .service("googleaistudio")
        .serviceSettings(JsonData.fromJson("{\"api_key\":\"api-key\",\"model_id\":\"model-id\"}"))
    )
);

Request example

Run `PUT _inference/completion/google_ai_studio_completion` to create an inference endpoint to perform a `completion` task type.

{
    "service": "googleaistudio",
    "service_settings": {
        "api_key": "api-key",
        "model_id": "model-id"
    }
}

Create a Google Vertex AI inference endpoint Generally available

PUT /_inference/{task_type}/{googlevertexai_inference_id}

Api key auth

Create an inference endpoint to perform an inference task with the googlevertexai service.

Required authorization

Cluster privileges: manage_inference

Path parameters

task_type string

The type of the inference task that the model will perform.

Values are rerank, text_embedding, completion, or chat_completion.
googlevertexai_inference_id string Required

The unique identifier of the inference endpoint.

Query parameters

timeout string

Specifies the amount of time to wait for the inference endpoint to be created.

Values are -1 or 0.

application/json

Body

chunking_settings object

Chunking configuration object
Hide chunking_settings attributes Show chunking_settings attributes object
- max_chunk_size number
  
  The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).
  
  Default value is 250.
- overlap number
  
  The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.
  
  Default value is 100.
- sentence_overlap number
  
  The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.
  
  Default value is 1.
- separator_group string Required
  
  This parameter is only applicable when using the recursive chunking strategy.
  
  Sets a predefined list of separators in the saved chunking settings based on the selected text type. Values can be markdown or plaintext.
  
  Using this parameter is an alternative to manually specifying a custom separators list.
- separators array[string] Required
  
  A list of strings used as possible split points when chunking text with the recursive strategy.
  
  Each string can be a plain string or a regular expression (regex) pattern. The system tries each separator in order to split the text, starting from the first item in the list.
  
  After splitting, it attempts to recombine smaller pieces into larger chunks that stay within the max_chunk_size limit, to reduce the total number of chunks generated.
- strategy string
  The chunking strategy: sentence, word, none or recursive.
  
  If strategy is set to recursive, you must also specify:
  
  max_chunk_size
  
  either separators orseparator_group
  
  Learn more about different chunking strategies in the linked documentation.
  Default value is sentence.
  
  External documentation
service string Required

Value is googlevertexai.
service_settings object Required
Hide service_settings attributes Show service_settings attributes object
- location string Required
  
  The name of the location to use for the inference task. Refer to the Google documentation for the list of supported locations.
  
  External documentation
- model_id string Required
  
  The name of the model to use for the inference task. Refer to the Google documentation for the list of supported models.
  
  External documentation
- project_id string Required
  
  The name of the project to use for the inference task.
- rate_limit object
  
  This setting helps to minimize the number of rate limit errors returned from the service.
  Hide rate_limit attribute Show rate_limit attribute object
  
  requests_per_minute number
  
  The number of requests allowed per minute. By default, the number of requests allowed per minute is set by each service as follows:
  
  alibabacloud-ai-search service: 1000
  
  anthropic service: 50
  
  azureaistudio service: 240
  
  azureopenai service and task type text_embedding: 1440
  
  azureopenai service and task type completion: 120
  
  cohere service: 10000
  
  elastic service and task type chat_completion: 240
  
  googleaistudio service: 360
  
  googlevertexai service: 30000
  
  hugging_face service: 3000
  
  jinaai service: 2000
  
  mistral service: 240
  
  openai service and task type text_embedding: 3000
  
  openai service and task type completion: 500
  
  voyageai service: 2000
  
  watsonxai service: 120
- service_account_json string Required
  
  A valid service account in JSON format for the Google Vertex AI API.
task_settings object
Hide task_settings attributes Show task_settings attributes object
- auto_truncate boolean
  
  For a text_embedding task, truncate inputs longer than the maximum token length automatically.
- top_n number
  
  For a rerank task, the number of the top N documents that should be returned.

Responses

200 application/json
Hide response attributes Show response attributes object
- chunking_settings object
  
  Chunking configuration object
  
  Hide chunking_settings attributes Show chunking_settings attributes object
  
  max_chunk_size number
  
  The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).
  
  Default value is 250.
  
  overlap number
  
  The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.
  
  Default value is 100.
  
  sentence_overlap number
  
  The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.
  
  Default value is 1.
  
  separator_group string Required
  
  This parameter is only applicable when using the recursive chunking strategy.
  
  Sets a predefined list of separators in the saved chunking settings based on the selected text type. Values can be markdown or plaintext.
  
  Using this parameter is an alternative to manually specifying a custom separators list.
  
  separators array[string] Required
  
  A list of strings used as possible split points when chunking text with the recursive strategy.
  
  Each string can be a plain string or a regular expression (regex) pattern. The system tries each separator in order to split the text, starting from the first item in the list.
  
  After splitting, it attempts to recombine smaller pieces into larger chunks that stay within the max_chunk_size limit, to reduce the total number of chunks generated.
  
  strategy string
  
  The chunking strategy: sentence, word, none or recursive.
  
  If strategy is set to recursive, you must also specify:
  
  max_chunk_size
  
  either separators orseparator_group
  
  Learn more about different chunking strategies in the linked documentation.
  
  Default value is sentence.
  
  External documentation
- service string Required
  
  The service type
- service_settings object Required
- task_settings object
- inference_id string Required
  
  The inference Id
- task_type string Required
  
  Values are text_embedding or rerank.

PUT /_inference/{task_type}/{googlevertexai_inference_id}

PUT _inference/text_embedding/google_vertex_ai_embeddingss
{
    "service": "googlevertexai",
    "service_settings": {
        "service_account_json": "service-account-json",
        "model_id": "model-id",
        "location": "location",
        "project_id": "project-id"
    }
}

resp = client.inference.put(
    task_type="text_embedding",
    inference_id="google_vertex_ai_embeddingss",
    inference_config={
        "service": "googlevertexai",
        "service_settings": {
            "service_account_json": "service-account-json",
            "model_id": "model-id",
            "location": "location",
            "project_id": "project-id"
        }
    },
)

const response = await client.inference.put({
  task_type: "text_embedding",
  inference_id: "google_vertex_ai_embeddingss",
  inference_config: {
    service: "googlevertexai",
    service_settings: {
      service_account_json: "service-account-json",
      model_id: "model-id",
      location: "location",
      project_id: "project-id",
    },
  },
});

response = client.inference.put(
  task_type: "text_embedding",
  inference_id: "google_vertex_ai_embeddingss",
  body: {
    "service": "googlevertexai",
    "service_settings": {
      "service_account_json": "service-account-json",
      "model_id": "model-id",
      "location": "location",
      "project_id": "project-id"
    }
  }
)

$resp = $client->inference()->put([
    "task_type" => "text_embedding",
    "inference_id" => "google_vertex_ai_embeddingss",
    "body" => [
        "service" => "googlevertexai",
        "service_settings" => [
            "service_account_json" => "service-account-json",
            "model_id" => "model-id",
            "location" => "location",
            "project_id" => "project-id",
        ],
    ],
]);

curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"googlevertexai","service_settings":{"service_account_json":"service-account-json","model_id":"model-id","location":"location","project_id":"project-id"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/google_vertex_ai_embeddingss"

client.inference().put(p -> p
    .inferenceId("google_vertex_ai_embeddingss")
    .taskType(TaskType.TextEmbedding)
    .inferenceConfig(i -> i
        .service("googlevertexai")
        .serviceSettings(JsonData.fromJson("{\"service_account_json\":\"service-account-json\",\"model_id\":\"model-id\",\"location\":\"location\",\"project_id\":\"project-id\"}"))
    )
);

Request examples

Run `PUT _inference/text_embedding/google_vertex_ai_embeddings` to create an inference endpoint to perform a `text_embedding` task type.

{
    "service": "googlevertexai",
    "service_settings": {
        "service_account_json": "service-account-json",
        "model_id": "model-id",
        "location": "location",
        "project_id": "project-id"
    }
}

Run `PUT _inference/rerank/google_vertex_ai_rerank` to create an inference endpoint to perform a `rerank` task type.

{
    "service": "googlevertexai",
    "service_settings": {
        "service_account_json": "service-account-json",
        "project_id": "project-id"
    }
}

Create a Watsonx inference endpoint Generally available

PUT /_inference/{task_type}/{watsonx_inference_id}

Api key auth

Create an inference endpoint to perform an inference task with the watsonxai service. You need an IBM Cloud Databases for Elasticsearch deployment to use the watsonxai inference service. You can provision one through the IBM catalog, the Cloud Databases CLI plug-in, the Cloud Databases API, or Terraform.

Required authorization

Cluster privileges: manage_inference

Path parameters

task_type string

The type of the inference task that the model will perform.

Values are text_embedding, chat_completion, or completion.
watsonx_inference_id string Required

The unique identifier of the inference endpoint.

Query parameters

timeout string

Specifies the amount of time to wait for the inference endpoint to be created.

Values are -1 or 0.

application/json

Body

service string Required

Value is watsonxai.
service_settings object Required
Hide service_settings attributes Show service_settings attributes object
- api_key string Required
  
  A valid API key of your Watsonx account. You can find your Watsonx API keys or you can create a new one on the API keys page.
  
  IMPORTANT: You need to provide the API key only once, during the inference model creation. The get inference endpoint API does not retrieve your API key. After creating the inference model, you cannot change the associated API key. If you want to use a different API key, delete the inference model and recreate it with the same name and the updated API key.
  
  External documentation
- api_version string Required
  
  A version parameter that takes a version date in the format of YYYY-MM-DD. For the active version data parameters, refer to the Wastonx documentation.
  
  External documentation
- model_id string Required
  
  The name of the model to use for the inference task. Refer to the IBM Embedding Models section in the Watsonx documentation for the list of available text embedding models. Refer to the IBM library - Foundation models in Watsonx.ai.
  
  External documentation
- project_id string Required
  
  The identifier of the IBM Cloud project to use for the inference task.
- rate_limit object
  
  This setting helps to minimize the number of rate limit errors returned from the service.
  Hide rate_limit attribute Show rate_limit attribute object
  
  requests_per_minute number
  
  The number of requests allowed per minute. By default, the number of requests allowed per minute is set by each service as follows:
  
  alibabacloud-ai-search service: 1000
  
  anthropic service: 50
  
  azureaistudio service: 240
  
  azureopenai service and task type text_embedding: 1440
  
  azureopenai service and task type completion: 120
  
  cohere service: 10000
  
  elastic service and task type chat_completion: 240
  
  googleaistudio service: 360
  
  googlevertexai service: 30000
  
  hugging_face service: 3000
  
  jinaai service: 2000
  
  mistral service: 240
  
  openai service and task type text_embedding: 3000
  
  openai service and task type completion: 500
  
  voyageai service: 2000
  
  watsonxai service: 120
- url string Required
  
  The URL of the inference endpoint that you created on Watsonx.

Responses

200 application/json
Hide response attributes Show response attributes object
- chunking_settings object
  
  Chunking configuration object
  
  Hide chunking_settings attributes Show chunking_settings attributes object
  
  max_chunk_size number
  
  The maximum size of a chunk in words. This value cannot be higher than 300 or lower than 20 (for sentence strategy) or 10 (for word strategy).
  
  Default value is 250.
  
  overlap number
  
  The number of overlapping words for chunks. It is applicable only to a word chunking strategy. This value cannot be higher than half the max_chunk_size value.
  
  Default value is 100.
  
  sentence_overlap number
  
  The number of overlapping sentences for chunks. It is applicable only for a sentence chunking strategy. It can be either 1 or 0.
  
  Default value is 1.
  
  separator_group string Required
  
  This parameter is only applicable when using the recursive chunking strategy.
  
  Sets a predefined list of separators in the saved chunking settings based on the selected text type. Values can be markdown or plaintext.
  
  Using this parameter is an alternative to manually specifying a custom separators list.
  
  separators array[string] Required
  
  A list of strings used as possible split points when chunking text with the recursive strategy.
  
  Each string can be a plain string or a regular expression (regex) pattern. The system tries each separator in order to split the text, starting from the first item in the list.
  
  After splitting, it attempts to recombine smaller pieces into larger chunks that stay within the max_chunk_size limit, to reduce the total number of chunks generated.
  
  strategy string
  
  The chunking strategy: sentence, word, none or recursive.
  
  If strategy is set to recursive, you must also specify:
  
  max_chunk_size
  
  either separators orseparator_group
  
  Learn more about different chunking strategies in the linked documentation.
  
  Default value is sentence.
  
  External documentation
- service string Required
  
  The service type
- service_settings object Required
- task_settings object
- inference_id string Required
  
  The inference Id
- task_type string Required
  
  Values are text_embedding, chat_completion, or completion.

PUT /_inference/{task_type}/{watsonx_inference_id}

PUT _inference/text_embedding/watsonx-embeddings
{
  "service": "watsonxai",
  "service_settings": {
      "api_key": "Watsonx-API-Key", 
      "url": "Wastonx-URL", 
      "model_id": "ibm/slate-30m-english-rtrvr",
      "project_id": "IBM-Cloud-ID", 
      "api_version": "2024-03-14"
  }
}

resp = client.inference.put(
    task_type="text_embedding",
    inference_id="watsonx-embeddings",
    inference_config={
        "service": "watsonxai",
        "service_settings": {
            "api_key": "Watsonx-API-Key",
            "url": "Wastonx-URL",
            "model_id": "ibm/slate-30m-english-rtrvr",
            "project_id": "IBM-Cloud-ID",
            "api_version": "2024-03-14"
        }
    },
)

const response = await client.inference.put({
  task_type: "text_embedding",
  inference_id: "watsonx-embeddings",
  inference_config: {
    service: "watsonxai",
    service_settings: {
      api_key: "Watsonx-API-Key",
      url: "Wastonx-URL",
      model_id: "ibm/slate-30m-english-rtrvr",
      project_id: "IBM-Cloud-ID",
      api_version: "2024-03-14",
    },
  },
});

response = client.inference.put(
  task_type: "text_embedding",
  inference_id: "watsonx-embeddings",
  body: {
    "service": "watsonxai",
    "service_settings": {
      "api_key": "Watsonx-API-Key",
      "url": "Wastonx-URL",
      "model_id": "ibm/slate-30m-english-rtrvr",
      "project_id": "IBM-Cloud-ID",
      "api_version": "2024-03-14"
    }
  }
)

$resp = $client->inference()->put([
    "task_type" => "text_embedding",
    "inference_id" => "watsonx-embeddings",
    "body" => [
        "service" => "watsonxai",
        "service_settings" => [
            "api_key" => "Watsonx-API-Key",
            "url" => "Wastonx-URL",
            "model_id" => "ibm/slate-30m-english-rtrvr",
            "project_id" => "IBM-Cloud-ID",
            "api_version" => "2024-03-14",
        ],
    ],
]);

curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"watsonxai","service_settings":{"api_key":"Watsonx-API-Key","url":"Wastonx-URL","model_id":"ibm/slate-30m-english-rtrvr","project_id":"IBM-Cloud-ID","api_version":"2024-03-14"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/watsonx-embeddings"

client.inference().put(p -> p
    .inferenceId("watsonx-embeddings")
    .taskType(TaskType.TextEmbedding)
    .inferenceConfig(i -> i
        .service("watsonxai")
        .serviceSettings(JsonData.fromJson("{\"api_key\":\"Watsonx-API-Key\",\"url\":\"Wastonx-URL\",\"model_id\":\"ibm/slate-30m-english-rtrvr\",\"project_id\":\"IBM-Cloud-ID\",\"api_version\":\"2024-03-14\"}"))
    )
);

Request example

Run `PUT _inference/text_embedding/watsonx-embeddings` to create an Watonsx inference endpoint that performs a text embedding task.

{
  "service": "watsonxai",
  "service_settings": {
      "api_key": "Watsonx-API-Key", 
      "url": "Wastonx-URL", 
      "model_id": "ibm/slate-30m-english-rtrvr",
      "project_id": "IBM-Cloud-ID", 
      "api_version": "2024-03-14"
  }
}

Delete a datafeed Generally available

DELETE /_ml/datafeeds/{datafeed_id}

Api key auth

Required authorization

Cluster privileges: manage_ml

Path parameters

datafeed_id string Required

A numerical character string that uniquely identifies the datafeed. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.

Query parameters

force boolean

Use to forcefully delete a started datafeed; this method is quicker than stopping and deleting the datafeed.

Responses

200 application/json
Hide response attribute Show response attribute object
- acknowledged boolean Required
  
  For a successful response, this value is always true. On failure, an exception is returned instead.

DELETE /_ml/datafeeds/{datafeed_id}

DELETE _ml/datafeeds/datafeed-total-requests

resp = client.ml.delete_datafeed(
    datafeed_id="datafeed-total-requests",
)

const response = await client.ml.deleteDatafeed({
  datafeed_id: "datafeed-total-requests",
});

response = client.ml.delete_datafeed(
  datafeed_id: "datafeed-total-requests"
)

$resp = $client->ml()->deleteDatafeed([
    "datafeed_id" => "datafeed-total-requests",
]);

curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_ml/datafeeds/datafeed-total-requests"

client.ml().deleteDatafeed(d -> d
    .datafeedId("datafeed-total-requests")
);

Response examples (200)

A successful response when deleting a datafeed.

{
  "acknowledged": true
}

Delete a filter Generally available

DELETE /_ml/filters/{filter_id}

Api key auth

If an anomaly detection job references the filter, you cannot delete the filter. You must update or delete the job before you can delete the filter.

Required authorization

Cluster privileges: manage_ml

Path parameters

filter_id string Required

A string that uniquely identifies a filter.

Responses

200 application/json
Hide response attribute Show response attribute object
- acknowledged boolean Required
  
  For a successful response, this value is always true. On failure, an exception is returned instead.

DELETE /_ml/filters/{filter_id}

DELETE _ml/filters/safe_domains

resp = client.ml.delete_filter(
    filter_id="safe_domains",
)

const response = await client.ml.deleteFilter({
  filter_id: "safe_domains",
});

response = client.ml.delete_filter(
  filter_id: "safe_domains"
)

$resp = $client->ml()->deleteFilter([
    "filter_id" => "safe_domains",
]);

curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_ml/filters/safe_domains"

client.ml().deleteFilter(d -> d
    .filterId("safe_domains")
);

Response examples (200)

A successful response when deleting a filter.

{
  "acknowledged": true
}

Open anomaly detection jobs Generally available

POST /_ml/anomaly_detectors/{job_id}/_open

Api key auth

An anomaly detection job must be opened to be ready to receive and analyze data. It can be opened and closed multiple times throughout its lifecycle. When you open a new job, it starts with an empty model. When you open an existing job, the most recent model state is automatically loaded. The job is ready to resume its analysis from where it left off, once new data is received.

Required authorization

Cluster privileges: manage_ml

Path parameters

job_id string Required

Identifier for the anomaly detection job.

Query parameters

timeout string

Controls the time to wait until a job has opened.

Values are -1 or 0.

application/json

Body

timeout string

A duration. Units can be nanos, micros, ms (milliseconds), s (seconds), m (minutes), h (hours) and d (days). Also accepts "0" without a unit and "-1" to indicate an unspecified value.

Responses

200 application/json
Hide response attributes Show response attributes object
- opened boolean Required
- node string Required

POST /_ml/anomaly_detectors/{job_id}/_open

POST /_ml/anomaly_detectors/job-01/_open
{
  "timeout": "35m"
}

resp = client.ml.open_job(
    job_id="job-01",
    timeout="35m",
)

const response = await client.ml.openJob({
  job_id: "job-01",
  timeout: "35m",
});

response = client.ml.open_job(
  job_id: "job-01",
  body: {
    "timeout": "35m"
  }
)

$resp = $client->ml()->openJob([
    "job_id" => "job-01",
    "body" => [
        "timeout" => "35m",
    ],
]);

curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"timeout":"35m"}' "$ELASTICSEARCH_URL/_ml/anomaly_detectors/job-01/_open"

client.ml().openJob(o -> o
    .jobId("job-01")
    .timeout(t -> t
        .time("35m")
    )
);

Request example

A request to open anomaly detection jobs. The timeout specifies to wait 35 minutes for the job to open.

{
  "timeout": "35m"
}

Response examples (200)

A successful response when opening an anomaly detection job.

{
  "opened": true,
  "node": "node-1"
}

Start datafeeds Generally available

POST /_ml/datafeeds/{datafeed_id}/_start

Api key auth

A datafeed must be started in order to retrieve data from Elasticsearch. A datafeed can be started and stopped multiple times throughout its lifecycle.

Before you can start a datafeed, the anomaly detection job must be open. Otherwise, an error occurs.

If you restart a stopped datafeed, it continues processing input data from the next millisecond after it was stopped. If new data was indexed for that exact millisecond between stopping and starting, it will be ignored.

When Elasticsearch security features are enabled, your datafeed remembers which roles the last user to create or update it had at the time of creation or update and runs the query using those same roles. If you provided secondary authorization headers when you created or updated the datafeed, those credentials are used instead.

Required authorization

Cluster privileges: manage_ml

Path parameters

datafeed_id string Required

A numerical character string that uniquely identifies the datafeed. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.

Query parameters

end string | number
The time that the datafeed should end, which can be specified by using one of the following formats:
- ISO 8601 format with milliseconds, for example 2017-01-22T06:00:00.000Z
- ISO 8601 format without milliseconds, for example 2017-01-22T06:00:00+00:00
- Milliseconds since the epoch, for example 1485061200000
Date-time arguments using either of the ISO 8601 formats must have a time zone designator, where Z is accepted as an abbreviation for UTC time. When a URL is expected (for example, in browsers), the + used in time zone designators must be encoded as %2B. The end time value is exclusive. If you do not specify an end time, the datafeed runs continuously.
start string | number

The time that the datafeed should begin, which can be specified by using the same formats as the end parameter. This value is inclusive. If you do not specify a start time and the datafeed is associated with a new anomaly detection job, the analysis starts from the earliest time for which data is available. If you restart a stopped datafeed and specify a start value that is earlier than the timestamp of the latest processed record, the datafeed continues from 1 millisecond after the timestamp of the latest processed record.
timeout string

Specifies the amount of time to wait until a datafeed starts.

Values are -1 or 0.

application/json

Body

end string | number

A date and time, either as a string whose format can depend on the context (defaulting to ISO 8601), or a number of milliseconds since the Epoch. Elasticsearch accepts both as input, but will generally output a string representation.

One of:
string-1 string UnitMillis number
start string | number

A date and time, either as a string whose format can depend on the context (defaulting to ISO 8601), or a number of milliseconds since the Epoch. Elasticsearch accepts both as input, but will generally output a string representation.

One of:
string-1 string UnitMillis number
timeout string

A duration. Units can be nanos, micros, ms (milliseconds), s (seconds), m (minutes), h (hours) and d (days). Also accepts "0" without a unit and "-1" to indicate an unspecified value.

Responses

200 application/json
Hide response attributes Show response attributes object
- node string | array[string] Required
  
  One of:
  NodeId string array-2 array[string]
- started boolean Required
  
  For a successful response, this value is always true. On failure, an exception is returned instead.

POST /_ml/datafeeds/{datafeed_id}/_start

POST _ml/datafeeds/datafeed-low_request_rate/_start
{
  "start": "2019-04-07T18:22:16Z"
}

resp = client.ml.start_datafeed(
    datafeed_id="datafeed-low_request_rate",
    start="2019-04-07T18:22:16Z",
)

const response = await client.ml.startDatafeed({
  datafeed_id: "datafeed-low_request_rate",
  start: "2019-04-07T18:22:16Z",
});

response = client.ml.start_datafeed(
  datafeed_id: "datafeed-low_request_rate",
  body: {
    "start": "2019-04-07T18:22:16Z"
  }
)

$resp = $client->ml()->startDatafeed([
    "datafeed_id" => "datafeed-low_request_rate",
    "body" => [
        "start" => "2019-04-07T18:22:16Z",
    ],
]);

curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"start":"2019-04-07T18:22:16Z"}' "$ELASTICSEARCH_URL/_ml/datafeeds/datafeed-low_request_rate/_start"

client.ml().startDatafeed(s -> s
    .datafeedId("datafeed-low_request_rate")
    .start(DateTime.of("2019-04-07T18:22:16Z"))
);

Request example

An example body for a `POST _ml/datafeeds/datafeed-low_request_rate/_start` request.

{
  "start": "2019-04-07T18:22:16Z"
}

Create a data frame analytics job Generally available

PUT /_ml/data_frame/analytics/{id}

Api key auth

This API creates a data frame analytics job that performs an analysis on the source indices and stores the outcome in a destination index. By default, the query used in the source configuration is {"match_all": {}}.

If the destination index does not exist, it is created automatically when you start the job.

If you supply only a subset of the regression or classification parameters, hyperparameter optimization occurs. It determines a value for each of the undefined parameters.

Required authorization

Index privileges: create_index,index,manage,read,view_index_metadata
Cluster privileges: manage_ml

Path parameters

id string Required

Identifier for the data frame analytics job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.

application/json

Body Required

allow_lazy_start boolean

Specifies whether this job can start when there is insufficient machine learning node capacity for it to be immediately assigned to a node. If set to false and a machine learning node with capacity to run the job cannot be immediately found, the API returns an error. If set to true, the API does not return an error; the job waits in the starting state until sufficient machine learning node capacity is available. This behavior is also affected by the cluster-wide xpack.ml.max_lazy_ml_nodes setting.

Default value is false.
analysis object Required
Hide analysis attributes Show analysis attributes object
- classification object
  Hide classification attributes Show classification attributes object
  
  alpha number
  
  Advanced configuration option. Machine learning uses loss guided tree growing, which means that the decision trees grow where the regularized loss decreases most quickly. This parameter affects loss calculations by acting as a multiplier of the tree depth. Higher alpha values result in shallower trees and faster training times. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to zero.
  
  dependent_variable string Required
  
  Defines which field of the document is to be predicted. It must match one of the fields in the index being used to train. If this field is missing from a document, then that document will not be used for training, but a prediction with the trained model will be generated for it. It is also known as continuous target variable. For classification analysis, the data type of the field must be numeric (integer, short, long, byte), categorical (ip or keyword), or boolean. There must be no more than 30 different values in this field. For regression analysis, the data type of the field must be numeric.
  
  downsample_factor number
  
  Advanced configuration option. Controls the fraction of data that is used to compute the derivatives of the loss function for tree training. A small value results in the use of a small fraction of the data. If this value is set to be less than 1, accuracy typically improves. However, too small a value may result in poor convergence for the ensemble and so require more trees. By default, this value is calculated during hyperparameter optimization. It must be greater than zero and less than or equal to 1.
  
  early_stopping_enabled boolean
  
  Advanced configuration option. Specifies whether the training process should finish if it is not finding any better performing models. If disabled, the training process can take significantly longer and the chance of finding a better performing model is unremarkable.
  
  Default value is true.
  
  eta number
  
  Advanced configuration option. The shrinkage applied to the weights. Smaller values result in larger forests which have a better generalization error. However, larger forests cause slower training. By default, this value is calculated during hyperparameter optimization. It must be a value between 0.001 and 1.
  
  eta_growth_rate_per_tree number
  
  Advanced configuration option. Specifies the rate at which eta increases for each new tree that is added to the forest. For example, a rate of 1.05 increases eta by 5% for each extra tree. By default, this value is calculated during hyperparameter optimization. It must be between 0.5 and 2.
  
  feature_bag_fraction number
  
  Advanced configuration option. Defines the fraction of features that will be used when selecting a random bag for each candidate split. By default, this value is calculated during hyperparameter optimization.
  
  feature_processors array[object]
  
  Advanced configuration option. A collection of feature preprocessors that modify one or more included fields. The analysis uses the resulting one or more features instead of the original document field. However, these features are ephemeral; they are not stored in the destination index. Multiple feature_processors entries can refer to the same document fields. Automatic categorical feature encoding still occurs for the fields that are unprocessed by a custom processor or that have categorical values. Use this property only if you want to override the automatic feature encoding of the specified fields.
  
  Hide feature_processors attributes Show feature_processors attributes object
  
  frequency_encoding object
  
  Hide frequency_encoding attributes Show frequency_encoding attributes object
  
  feature_name string Required
  
  field string Required
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  frequency_map object Required
  
  The resulting frequency map for the field value. If the field value is missing from the frequency_map, the resulting value is 0.
  
  multi_encoding object
  
  Hide multi_encoding attribute Show multi_encoding attribute object
  
  processors array[number] Required
  
  The ordered array of custom processors to execute. Must be more than 1.
  
  n_gram_encoding object
  
  Hide n_gram_encoding attributes Show n_gram_encoding attributes object
  
  feature_prefix string
  
  The feature name prefix. Defaults to ngram__.
  
  field string Required
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  length number
  
  Specifies the length of the n-gram substring. Defaults to 50. Must be greater than 0.
  
  n_grams array[number] Required
  
  Specifies which n-grams to gather. It’s an array of integer values where the minimum value is 1, and a maximum value is 5.
  
  start number
  
  Specifies the zero-indexed start of the n-gram substring. Negative values are allowed for encoding n-grams of string suffixes. Defaults to 0.
  
  custom boolean
  
  one_hot_encoding object
  
  Hide one_hot_encoding attributes Show one_hot_encoding attributes object
  
  field string Required
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  hot_map string Required
  
  The one hot map mapping the field value with the column name.
  
  target_mean_encoding object
  
  Hide target_mean_encoding attributes Show target_mean_encoding attributes object
  
  default_value number Required
  
  The default value if field value is not found in the target_map.
  
  feature_name string Required
  
  field string Required
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  target_map object Required
  
  The field value to target mean transition map.
  
  gamma number
  
  Advanced configuration option. Regularization parameter to prevent overfitting on the training data set. Multiplies a linear penalty associated with the size of individual trees in the forest. A high gamma value causes training to prefer small trees. A small gamma value results in larger individual trees and slower training. By default, this value is calculated during hyperparameter optimization. It must be a nonnegative value.
  
  lambda number
  
  Advanced configuration option. Regularization parameter to prevent overfitting on the training data set. Multiplies an L2 regularization term which applies to leaf weights of the individual trees in the forest. A high lambda value causes training to favor small leaf weights. This behavior makes the prediction function smoother at the expense of potentially not being able to capture relevant relationships between the features and the dependent variable. A small lambda value results in large individual trees and slower training. By default, this value is calculated during hyperparameter optimization. It must be a nonnegative value.
  
  max_optimization_rounds_per_hyperparameter number
  
  Advanced configuration option. A multiplier responsible for determining the maximum number of hyperparameter optimization steps in the Bayesian optimization procedure. The maximum number of steps is determined based on the number of undefined hyperparameters times the maximum optimization rounds per hyperparameter. By default, this value is calculated during hyperparameter optimization.
  
  max_trees number
  
  Advanced configuration option. Defines the maximum number of decision trees in the forest. The maximum value is 2000. By default, this value is calculated during hyperparameter optimization.
  
  num_top_feature_importance_values number
  
  Advanced configuration option. Specifies the maximum number of feature importance values per document to return. By default, no feature importance calculation occurs.
  
  Default value is 0.
  
  prediction_field_name string
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  randomize_seed number
  
  Defines the seed for the random generator that is used to pick training data. By default, it is randomly generated. Set it to a specific value to use the same training data each time you start a job (assuming other related parameters such as source and analyzed_fields are the same).
  
  soft_tree_depth_limit number
  
  Advanced configuration option. Machine learning uses loss guided tree growing, which means that the decision trees grow where the regularized loss decreases most quickly. This soft limit combines with the soft_tree_depth_tolerance to penalize trees that exceed the specified depth; the regularized loss increases quickly beyond this depth. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to 0.
  
  soft_tree_depth_tolerance number
  
  Advanced configuration option. This option controls how quickly the regularized loss increases when the tree depth exceeds soft_tree_depth_limit. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to 0.01.
  
  training_percent string | number
  
  One of:
  string-1 string number-2 number
  
  class_assignment_objective string
  
  num_top_classes number
  
  Defines the number of categories for which the predicted probabilities are reported. It must be non-negative or -1. If it is -1 or greater than the total number of categories, probabilities are reported for all categories; if you have a large number of categories, there could be a significant effect on the size of your destination index. NOTE: To use the AUC ROC evaluation method, num_top_classes must be set to -1 or a value greater than or equal to the total number of categories.
  
  Default value is 2.
- outlier_detection object
  Hide outlier_detection attributes Show outlier_detection attributes object
  
  compute_feature_influence boolean
  
  Specifies whether the feature influence calculation is enabled.
  
  Default value is true.
  
  feature_influence_threshold number
  
  The minimum outlier score that a document needs to have in order to calculate its feature influence score. Value range: 0-1.
  
  Default value is 0.1.
  
  method string
  
  The method that outlier detection uses. Available methods are lof, ldof, distance_kth_nn, distance_knn, and ensemble. The default value is ensemble, which means that outlier detection uses an ensemble of different methods and normalises and combines their individual outlier scores to obtain the overall outlier score.
  
  Default value is ensemble.
  
  n_neighbors number
  
  Defines the value for how many nearest neighbors each method of outlier detection uses to calculate its outlier score. When the value is not set, different values are used for different ensemble members. This default behavior helps improve the diversity in the ensemble; only override it if you are confident that the value you choose is appropriate for the data set.
  
  outlier_fraction number
  
  The proportion of the data set that is assumed to be outlying prior to outlier detection. For example, 0.05 means it is assumed that 5% of values are real outliers and 95% are inliers.
  
  standardization_enabled boolean
  
  If true, the following operation is performed on the columns before computing outlier scores: (x_i - mean(x_i)) / sd(x_i).
  
  Default value is true.
- regression object
  Hide regression attributes Show regression attributes object
  
  alpha number
  
  Advanced configuration option. Machine learning uses loss guided tree growing, which means that the decision trees grow where the regularized loss decreases most quickly. This parameter affects loss calculations by acting as a multiplier of the tree depth. Higher alpha values result in shallower trees and faster training times. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to zero.
  
  dependent_variable string Required
  
  Defines which field of the document is to be predicted. It must match one of the fields in the index being used to train. If this field is missing from a document, then that document will not be used for training, but a prediction with the trained model will be generated for it. It is also known as continuous target variable. For classification analysis, the data type of the field must be numeric (integer, short, long, byte), categorical (ip or keyword), or boolean. There must be no more than 30 different values in this field. For regression analysis, the data type of the field must be numeric.
  
  downsample_factor number
  
  Advanced configuration option. Controls the fraction of data that is used to compute the derivatives of the loss function for tree training. A small value results in the use of a small fraction of the data. If this value is set to be less than 1, accuracy typically improves. However, too small a value may result in poor convergence for the ensemble and so require more trees. By default, this value is calculated during hyperparameter optimization. It must be greater than zero and less than or equal to 1.
  
  early_stopping_enabled boolean
  
  Advanced configuration option. Specifies whether the training process should finish if it is not finding any better performing models. If disabled, the training process can take significantly longer and the chance of finding a better performing model is unremarkable.
  
  Default value is true.
  
  eta number
  
  Advanced configuration option. The shrinkage applied to the weights. Smaller values result in larger forests which have a better generalization error. However, larger forests cause slower training. By default, this value is calculated during hyperparameter optimization. It must be a value between 0.001 and 1.
  
  eta_growth_rate_per_tree number
  
  Advanced configuration option. Specifies the rate at which eta increases for each new tree that is added to the forest. For example, a rate of 1.05 increases eta by 5% for each extra tree. By default, this value is calculated during hyperparameter optimization. It must be between 0.5 and 2.
  
  feature_bag_fraction number
  
  Advanced configuration option. Defines the fraction of features that will be used when selecting a random bag for each candidate split. By default, this value is calculated during hyperparameter optimization.
  
  feature_processors array[object]
  
  Advanced configuration option. A collection of feature preprocessors that modify one or more included fields. The analysis uses the resulting one or more features instead of the original document field. However, these features are ephemeral; they are not stored in the destination index. Multiple feature_processors entries can refer to the same document fields. Automatic categorical feature encoding still occurs for the fields that are unprocessed by a custom processor or that have categorical values. Use this property only if you want to override the automatic feature encoding of the specified fields.
  
  Hide feature_processors attributes Show feature_processors attributes object
  
  frequency_encoding object
  
  Hide frequency_encoding attributes Show frequency_encoding attributes object
  
  feature_name string Required
  
  field string Required
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  frequency_map object Required
  
  The resulting frequency map for the field value. If the field value is missing from the frequency_map, the resulting value is 0.
  
  multi_encoding object
  
  Hide multi_encoding attribute Show multi_encoding attribute object
  
  processors array[number] Required
  
  The ordered array of custom processors to execute. Must be more than 1.
  
  n_gram_encoding object
  
  Hide n_gram_encoding attributes Show n_gram_encoding attributes object
  
  feature_prefix string
  
  The feature name prefix. Defaults to ngram__.
  
  field string Required
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  length number
  
  Specifies the length of the n-gram substring. Defaults to 50. Must be greater than 0.
  
  n_grams array[number] Required
  
  Specifies which n-grams to gather. It’s an array of integer values where the minimum value is 1, and a maximum value is 5.
  
  start number
  
  Specifies the zero-indexed start of the n-gram substring. Negative values are allowed for encoding n-grams of string suffixes. Defaults to 0.
  
  custom boolean
  
  one_hot_encoding object
  
  Hide one_hot_encoding attributes Show one_hot_encoding attributes object
  
  field string Required
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  hot_map string Required
  
  The one hot map mapping the field value with the column name.
  
  target_mean_encoding object
  
  Hide target_mean_encoding attributes Show target_mean_encoding attributes object
  
  default_value number Required
  
  The default value if field value is not found in the target_map.
  
  feature_name string Required
  
  field string Required
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  target_map object Required
  
  The field value to target mean transition map.
  
  gamma number
  
  Advanced configuration option. Regularization parameter to prevent overfitting on the training data set. Multiplies a linear penalty associated with the size of individual trees in the forest. A high gamma value causes training to prefer small trees. A small gamma value results in larger individual trees and slower training. By default, this value is calculated during hyperparameter optimization. It must be a nonnegative value.
  
  lambda number
  
  Advanced configuration option. Regularization parameter to prevent overfitting on the training data set. Multiplies an L2 regularization term which applies to leaf weights of the individual trees in the forest. A high lambda value causes training to favor small leaf weights. This behavior makes the prediction function smoother at the expense of potentially not being able to capture relevant relationships between the features and the dependent variable. A small lambda value results in large individual trees and slower training. By default, this value is calculated during hyperparameter optimization. It must be a nonnegative value.
  
  max_optimization_rounds_per_hyperparameter number
  
  Advanced configuration option. A multiplier responsible for determining the maximum number of hyperparameter optimization steps in the Bayesian optimization procedure. The maximum number of steps is determined based on the number of undefined hyperparameters times the maximum optimization rounds per hyperparameter. By default, this value is calculated during hyperparameter optimization.
  
  max_trees number
  
  Advanced configuration option. Defines the maximum number of decision trees in the forest. The maximum value is 2000. By default, this value is calculated during hyperparameter optimization.
  
  num_top_feature_importance_values number
  
  Advanced configuration option. Specifies the maximum number of feature importance values per document to return. By default, no feature importance calculation occurs.
  
  Default value is 0.
  
  prediction_field_name string
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  randomize_seed number
  
  Defines the seed for the random generator that is used to pick training data. By default, it is randomly generated. Set it to a specific value to use the same training data each time you start a job (assuming other related parameters such as source and analyzed_fields are the same).
  
  soft_tree_depth_limit number
  
  Advanced configuration option. Machine learning uses loss guided tree growing, which means that the decision trees grow where the regularized loss decreases most quickly. This soft limit combines with the soft_tree_depth_tolerance to penalize trees that exceed the specified depth; the regularized loss increases quickly beyond this depth. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to 0.
  
  soft_tree_depth_tolerance number
  
  Advanced configuration option. This option controls how quickly the regularized loss increases when the tree depth exceeds soft_tree_depth_limit. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to 0.01.
  
  training_percent string | number
  
  One of:
  string-1 string number-2 number
  
  loss_function string
  
  The loss function used during regression. Available options are mse (mean squared error), msle (mean squared logarithmic error), huber (Pseudo-Huber loss).
  
  Default value is mse.
  
  loss_function_parameter number
  
  A positive number that is used as a parameter to the loss_function.
analyzed_fields object
Hide analyzed_fields attributes Show analyzed_fields attributes object
- includes array[string]
  
  An array of strings that defines the fields that will be excluded from the analysis. You do not need to add fields with unsupported data types to excludes, these fields are excluded from the analysis automatically.
- excludes array[string]
  
  An array of strings that defines the fields that will be included in the analysis.
description string

A description of the job.
dest object Required
Hide dest attributes Show dest attributes object
- index string Required
- results_field string
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
max_num_threads number

The maximum number of threads to be used by the analysis. Using more threads may decrease the time necessary to complete the analysis at the cost of using more CPU. Note that the process may use additional threads for operational functionality other than the analysis itself.

Default value is 1.
_meta object
Hide _meta attribute Show _meta attribute object
- * object Additional properties
model_memory_limit string

The approximate maximum amount of memory resources that are permitted for analytical processing. If your elasticsearch.yml file contains an xpack.ml.max_model_memory_limit setting, an error occurs when you try to create data frame analytics jobs that have model_memory_limit values greater than that setting.

Default value is 1gb.
source object Required
Hide source attributes Show source attributes object
- index string | array[string] Required
- runtime_mappings object
  Hide runtime_mappings attribute Show runtime_mappings attribute object
  
  * object Additional properties
  
  Hide * attributes Show * attributes object
  
  fields object
  
  For type composite
  
  Hide fields attribute Show fields attribute object
  
  * object Additional properties
  
  Hide * attribute Show * attribute object
  
  type string Required
  
  Values are boolean, composite, date, double, geo_point, geo_shape, ip, keyword, long, or lookup.
  
  fetch_fields array[object]
  
  For type lookup
  
  Hide fetch_fields attributes Show fetch_fields attributes object
  
  field string Required
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  format string
  
  format string
  
  A custom format for date type runtime fields.
  
  input_field string
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  target_field string
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  target_index string
  
  script object
  
  Hide script attributes Show script attributes object
  
  source string | object
  
  One of:
  string-1 string SearchRequestBody object
  
  Hide attributes Show attributes
  
  aggregations object
  
  Defines the aggregations that are run as part of the search request.
  
  collapse object
  
  explain boolean
  
  If true, the request returns detailed information about score computation as part of a hit.
  
  Default value is false.
  
  ext object
  
  Configuration of search extensions defined by Elasticsearch plugins.
  
  from number
  
  The starting document offset, which must be non-negative. By default, you cannot page through more than 10,000 hits using the from and size parameters. To page through more hits, use the search_after parameter.
  
  Default value is 0.
  
  highlight
  
  track_total_hits boolean | number
  
  Number of hits matching the query to count accurately. If true, the exact number of hits is returned at the cost of some performance. If false, the response does not include the total number of hits matching the query. Defaults to 10,000 hits.
  
  indices_boost array[object]
  
  Boost the _score of documents from specified indices. The boost value is the factor by which scores are multiplied. A boost value greater than 1.0 increases the score. A boost value between 0 and 1.0 decreases the score.
  
  docvalue_fields array[object]
  
  An array of wildcard (*) field patterns. The request returns doc values for field names matching these patterns in the hits.fields property of the response.
  
  knn
  
  min_score number
  
  The minimum _score for matching documents. Documents with a lower _score are not included in search results or results collected by aggregations.
  
  post_filter object
  
  An Elasticsearch Query DSL (Domain Specific Language) object that defines a query.
  
  profile boolean
  
  Set to true to return detailed timing information about the execution of individual components in a search request. NOTE: This is a debugging tool and adds significant overhead to search execution.
  
  Default value is false.
  
  query object
  
  An Elasticsearch Query DSL (Domain Specific Language) object that defines a query.
  
  rescore
  
  retriever object
  
  script_fields object
  
  Retrieve a script evaluation (based on different fields) for each hit.
  
  search_after array[number | string | boolean | null]
  
  A field value.
  
  size number
  
  The number of hits to return, which must not be negative. By default, you cannot page through more than 10,000 hits using the from and size parameters. To page through more hits, use the search_after property.
  
  Default value is 10.
  
  slice object
  
  sort
  
  _source
  
  fields array[object]
  
  An array of wildcard (*) field patterns. The request returns values for field names matching these patterns in the hits.fields property of the response.
  
  suggest object
  
  terminate_after number
  
  The maximum number of documents to collect for each shard. If a query reaches this limit, Elasticsearch terminates the query early. Elasticsearch collects documents before sorting.
  
  IMPORTANT: Use with caution. Elasticsearch applies this property to each shard handling the request. When possible, let Elasticsearch perform early termination automatically. Avoid specifying this property for requests that target data streams with backing indices across multiple data tiers.
  
  If set to 0 (default), the query does not terminate early.
  
  Default value is 0.
  
  timeout string
  
  The period of time to wait for a response from each shard. If no response is received before the timeout expires, the request fails and returns an error. Defaults to no timeout.
  
  track_scores boolean
  
  If true, calculate and return document scores, even if the scores are not used for sorting.
  
  Default value is false.
  
  version boolean
  
  If true, the request returns the document version as part of a hit.
  
  Default value is false.
  
  seq_no_primary_term boolean
  
  If true, the request returns sequence number and primary term of the last modification of each hit.
  
  stored_fields string | array[string]
  
  pit object
  
  runtime_mappings object
  
  stats array[string]
  
  The stats groups to associate with the search. Each group maintains a statistics aggregation for its associated searches. You can retrieve these stats using the indices stats API.
  
  id string
  
  params object
  
  Specifies any named parameters that are passed into the script as variables. Use parameters instead of hard-coded values to decrease compile time.
  
  Hide params attribute Show params attribute object
  
  * object Additional properties
  
  lang string
  
  Any of:
  string-1 string string-2 string
  
  Values are painless, expression, mustache, or java.
  
  options object
  
  Hide options attribute Show options attribute object
  
  * string Additional properties
  
  type string Required
  
  Values are boolean, composite, date, double, geo_point, geo_shape, ip, keyword, long, or lookup.
- _source object
  Hide _source attributes Show _source attributes object
  
  includes array[string]
  
  An array of strings that defines the fields that will be excluded from the analysis. You do not need to add fields with unsupported data types to excludes, these fields are excluded from the analysis automatically.
  
  excludes array[string]
  
  An array of strings that defines the fields that will be included in the analysis.
- query object
  
  The Elasticsearch query domain-specific language (DSL). This value corresponds to the query object in an Elasticsearch search POST body. All the options that are supported by Elasticsearch can be used, as this object is passed verbatim to Elasticsearch. By default, this property has the following value: {"match_all": {}}.
  
  Query DSL
headers object
version string

Responses

200 application/json
Hide response attributes Show response attributes object
- authorization object
  
  Hide authorization attributes Show authorization attributes object
  
  api_key object
  
  Hide api_key attributes Show api_key attributes object
  
  id string Required
  
  The identifier for the API key.
  
  name string Required
  
  The name of the API key.
  
  roles array[string]
  
  If a user ID was used for the most recent update to the job, its roles at the time of the update are listed in the response.
  
  service_account string
  
  If a service account was used for the most recent update to the job, the account name is listed in the response.
- allow_lazy_start boolean Required
- analysis object Required
  
  Hide analysis attributes Show analysis attributes object
  
  classification object
  
  Hide classification attributes Show classification attributes object
  
  alpha number
  
  Advanced configuration option. Machine learning uses loss guided tree growing, which means that the decision trees grow where the regularized loss decreases most quickly. This parameter affects loss calculations by acting as a multiplier of the tree depth. Higher alpha values result in shallower trees and faster training times. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to zero.
  
  dependent_variable string Required
  
  Defines which field of the document is to be predicted. It must match one of the fields in the index being used to train. If this field is missing from a document, then that document will not be used for training, but a prediction with the trained model will be generated for it. It is also known as continuous target variable. For classification analysis, the data type of the field must be numeric (integer, short, long, byte), categorical (ip or keyword), or boolean. There must be no more than 30 different values in this field. For regression analysis, the data type of the field must be numeric.
  
  downsample_factor number
  
  Advanced configuration option. Controls the fraction of data that is used to compute the derivatives of the loss function for tree training. A small value results in the use of a small fraction of the data. If this value is set to be less than 1, accuracy typically improves. However, too small a value may result in poor convergence for the ensemble and so require more trees. By default, this value is calculated during hyperparameter optimization. It must be greater than zero and less than or equal to 1.
  
  early_stopping_enabled boolean
  
  Advanced configuration option. Specifies whether the training process should finish if it is not finding any better performing models. If disabled, the training process can take significantly longer and the chance of finding a better performing model is unremarkable.
  
  Default value is true.
  
  eta number
  
  Advanced configuration option. The shrinkage applied to the weights. Smaller values result in larger forests which have a better generalization error. However, larger forests cause slower training. By default, this value is calculated during hyperparameter optimization. It must be a value between 0.001 and 1.
  
  eta_growth_rate_per_tree number
  
  Advanced configuration option. Specifies the rate at which eta increases for each new tree that is added to the forest. For example, a rate of 1.05 increases eta by 5% for each extra tree. By default, this value is calculated during hyperparameter optimization. It must be between 0.5 and 2.
  
  feature_bag_fraction number
  
  Advanced configuration option. Defines the fraction of features that will be used when selecting a random bag for each candidate split. By default, this value is calculated during hyperparameter optimization.
  
  feature_processors array[object]
  
  Advanced configuration option. A collection of feature preprocessors that modify one or more included fields. The analysis uses the resulting one or more features instead of the original document field. However, these features are ephemeral; they are not stored in the destination index. Multiple feature_processors entries can refer to the same document fields. Automatic categorical feature encoding still occurs for the fields that are unprocessed by a custom processor or that have categorical values. Use this property only if you want to override the automatic feature encoding of the specified fields.
  
  Hide feature_processors attributes Show feature_processors attributes object
  
  frequency_encoding object
  
  multi_encoding object
  
  n_gram_encoding object
  
  one_hot_encoding object
  
  target_mean_encoding object
  
  gamma number
  
  Advanced configuration option. Regularization parameter to prevent overfitting on the training data set. Multiplies a linear penalty associated with the size of individual trees in the forest. A high gamma value causes training to prefer small trees. A small gamma value results in larger individual trees and slower training. By default, this value is calculated during hyperparameter optimization. It must be a nonnegative value.
  
  lambda number
  
  Advanced configuration option. Regularization parameter to prevent overfitting on the training data set. Multiplies an L2 regularization term which applies to leaf weights of the individual trees in the forest. A high lambda value causes training to favor small leaf weights. This behavior makes the prediction function smoother at the expense of potentially not being able to capture relevant relationships between the features and the dependent variable. A small lambda value results in large individual trees and slower training. By default, this value is calculated during hyperparameter optimization. It must be a nonnegative value.
  
  max_optimization_rounds_per_hyperparameter number
  
  Advanced configuration option. A multiplier responsible for determining the maximum number of hyperparameter optimization steps in the Bayesian optimization procedure. The maximum number of steps is determined based on the number of undefined hyperparameters times the maximum optimization rounds per hyperparameter. By default, this value is calculated during hyperparameter optimization.
  
  max_trees number
  
  Advanced configuration option. Defines the maximum number of decision trees in the forest. The maximum value is 2000. By default, this value is calculated during hyperparameter optimization.
  
  num_top_feature_importance_values number
  
  Advanced configuration option. Specifies the maximum number of feature importance values per document to return. By default, no feature importance calculation occurs.
  
  Default value is 0.
  
  prediction_field_name string
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  randomize_seed number
  
  Defines the seed for the random generator that is used to pick training data. By default, it is randomly generated. Set it to a specific value to use the same training data each time you start a job (assuming other related parameters such as source and analyzed_fields are the same).
  
  soft_tree_depth_limit number
  
  Advanced configuration option. Machine learning uses loss guided tree growing, which means that the decision trees grow where the regularized loss decreases most quickly. This soft limit combines with the soft_tree_depth_tolerance to penalize trees that exceed the specified depth; the regularized loss increases quickly beyond this depth. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to 0.
  
  soft_tree_depth_tolerance number
  
  Advanced configuration option. This option controls how quickly the regularized loss increases when the tree depth exceeds soft_tree_depth_limit. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to 0.01.
  
  training_percent string | number
  
  One of:
  string-1 string number-2 number
  
  class_assignment_objective string
  
  num_top_classes number
  
  Defines the number of categories for which the predicted probabilities are reported. It must be non-negative or -1. If it is -1 or greater than the total number of categories, probabilities are reported for all categories; if you have a large number of categories, there could be a significant effect on the size of your destination index. NOTE: To use the AUC ROC evaluation method, num_top_classes must be set to -1 or a value greater than or equal to the total number of categories.
  
  Default value is 2.
  
  outlier_detection object
  
  Hide outlier_detection attributes Show outlier_detection attributes object
  
  compute_feature_influence boolean
  
  Specifies whether the feature influence calculation is enabled.
  
  Default value is true.
  
  feature_influence_threshold number
  
  The minimum outlier score that a document needs to have in order to calculate its feature influence score. Value range: 0-1.
  
  Default value is 0.1.
  
  method string
  
  The method that outlier detection uses. Available methods are lof, ldof, distance_kth_nn, distance_knn, and ensemble. The default value is ensemble, which means that outlier detection uses an ensemble of different methods and normalises and combines their individual outlier scores to obtain the overall outlier score.
  
  Default value is ensemble.
  
  n_neighbors number
  
  Defines the value for how many nearest neighbors each method of outlier detection uses to calculate its outlier score. When the value is not set, different values are used for different ensemble members. This default behavior helps improve the diversity in the ensemble; only override it if you are confident that the value you choose is appropriate for the data set.
  
  outlier_fraction number
  
  The proportion of the data set that is assumed to be outlying prior to outlier detection. For example, 0.05 means it is assumed that 5% of values are real outliers and 95% are inliers.
  
  standardization_enabled boolean
  
  If true, the following operation is performed on the columns before computing outlier scores: (x_i - mean(x_i)) / sd(x_i).
  
  Default value is true.
  
  regression object
  
  Hide regression attributes Show regression attributes object
  
  alpha number
  
  Advanced configuration option. Machine learning uses loss guided tree growing, which means that the decision trees grow where the regularized loss decreases most quickly. This parameter affects loss calculations by acting as a multiplier of the tree depth. Higher alpha values result in shallower trees and faster training times. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to zero.
  
  dependent_variable string Required
  
  Defines which field of the document is to be predicted. It must match one of the fields in the index being used to train. If this field is missing from a document, then that document will not be used for training, but a prediction with the trained model will be generated for it. It is also known as continuous target variable. For classification analysis, the data type of the field must be numeric (integer, short, long, byte), categorical (ip or keyword), or boolean. There must be no more than 30 different values in this field. For regression analysis, the data type of the field must be numeric.
  
  downsample_factor number
  
  Advanced configuration option. Controls the fraction of data that is used to compute the derivatives of the loss function for tree training. A small value results in the use of a small fraction of the data. If this value is set to be less than 1, accuracy typically improves. However, too small a value may result in poor convergence for the ensemble and so require more trees. By default, this value is calculated during hyperparameter optimization. It must be greater than zero and less than or equal to 1.
  
  early_stopping_enabled boolean
  
  Advanced configuration option. Specifies whether the training process should finish if it is not finding any better performing models. If disabled, the training process can take significantly longer and the chance of finding a better performing model is unremarkable.
  
  Default value is true.
  
  eta number
  
  Advanced configuration option. The shrinkage applied to the weights. Smaller values result in larger forests which have a better generalization error. However, larger forests cause slower training. By default, this value is calculated during hyperparameter optimization. It must be a value between 0.001 and 1.
  
  eta_growth_rate_per_tree number
  
  Advanced configuration option. Specifies the rate at which eta increases for each new tree that is added to the forest. For example, a rate of 1.05 increases eta by 5% for each extra tree. By default, this value is calculated during hyperparameter optimization. It must be between 0.5 and 2.
  
  feature_bag_fraction number
  
  Advanced configuration option. Defines the fraction of features that will be used when selecting a random bag for each candidate split. By default, this value is calculated during hyperparameter optimization.
  
  feature_processors array[object]
  
  Advanced configuration option. A collection of feature preprocessors that modify one or more included fields. The analysis uses the resulting one or more features instead of the original document field. However, these features are ephemeral; they are not stored in the destination index. Multiple feature_processors entries can refer to the same document fields. Automatic categorical feature encoding still occurs for the fields that are unprocessed by a custom processor or that have categorical values. Use this property only if you want to override the automatic feature encoding of the specified fields.
  
  Hide feature_processors attributes Show feature_processors attributes object
  
  frequency_encoding object
  
  multi_encoding object
  
  n_gram_encoding object
  
  one_hot_encoding object
  
  target_mean_encoding object
  
  gamma number
  
  Advanced configuration option. Regularization parameter to prevent overfitting on the training data set. Multiplies a linear penalty associated with the size of individual trees in the forest. A high gamma value causes training to prefer small trees. A small gamma value results in larger individual trees and slower training. By default, this value is calculated during hyperparameter optimization. It must be a nonnegative value.
  
  lambda number
  
  Advanced configuration option. Regularization parameter to prevent overfitting on the training data set. Multiplies an L2 regularization term which applies to leaf weights of the individual trees in the forest. A high lambda value causes training to favor small leaf weights. This behavior makes the prediction function smoother at the expense of potentially not being able to capture relevant relationships between the features and the dependent variable. A small lambda value results in large individual trees and slower training. By default, this value is calculated during hyperparameter optimization. It must be a nonnegative value.
  
  max_optimization_rounds_per_hyperparameter number
  
  Advanced configuration option. A multiplier responsible for determining the maximum number of hyperparameter optimization steps in the Bayesian optimization procedure. The maximum number of steps is determined based on the number of undefined hyperparameters times the maximum optimization rounds per hyperparameter. By default, this value is calculated during hyperparameter optimization.
  
  max_trees number
  
  Advanced configuration option. Defines the maximum number of decision trees in the forest. The maximum value is 2000. By default, this value is calculated during hyperparameter optimization.
  
  num_top_feature_importance_values number
  
  Advanced configuration option. Specifies the maximum number of feature importance values per document to return. By default, no feature importance calculation occurs.
  
  Default value is 0.
  
  prediction_field_name string
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  randomize_seed number
  
  Defines the seed for the random generator that is used to pick training data. By default, it is randomly generated. Set it to a specific value to use the same training data each time you start a job (assuming other related parameters such as source and analyzed_fields are the same).
  
  soft_tree_depth_limit number
  
  Advanced configuration option. Machine learning uses loss guided tree growing, which means that the decision trees grow where the regularized loss decreases most quickly. This soft limit combines with the soft_tree_depth_tolerance to penalize trees that exceed the specified depth; the regularized loss increases quickly beyond this depth. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to 0.
  
  soft_tree_depth_tolerance number
  
  Advanced configuration option. This option controls how quickly the regularized loss increases when the tree depth exceeds soft_tree_depth_limit. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to 0.01.
  
  training_percent string | number
  
  One of:
  string-1 string number-2 number
  
  loss_function string
  
  The loss function used during regression. Available options are mse (mean squared error), msle (mean squared logarithmic error), huber (Pseudo-Huber loss).
  
  Default value is mse.
  
  loss_function_parameter number
  
  A positive number that is used as a parameter to the loss_function.
- analyzed_fields object
  
  Hide analyzed_fields attributes Show analyzed_fields attributes object
  
  includes array[string]
  
  An array of strings that defines the fields that will be excluded from the analysis. You do not need to add fields with unsupported data types to excludes, these fields are excluded from the analysis automatically.
  
  excludes array[string]
  
  An array of strings that defines the fields that will be included in the analysis.
- create_time number
  
  Time unit for milliseconds
- description string
- dest object Required
  
  Hide dest attributes Show dest attributes object
  
  index string Required
  
  results_field string
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
- id string Required
- max_num_threads number Required
- _meta object
  
  Hide _meta attribute Show _meta attribute object
  
  * object Additional properties
- model_memory_limit string Required
- source object Required
  
  Hide source attributes Show source attributes object
  
  index string | array[string] Required
  
  runtime_mappings object
  
  Hide runtime_mappings attribute Show runtime_mappings attribute object
  
  * object Additional properties
  
  Hide * attributes Show * attributes object
  
  fields object
  
  For type composite
  
  Hide fields attribute Show fields attribute object
  
  * object Additional properties
  
  Hide * attribute Show * attribute object
  
  type string Required
  
  Values are boolean, composite, date, double, geo_point, geo_shape, ip, keyword, long, or lookup.
  
  fetch_fields array[object]
  
  For type lookup
  
  Hide fetch_fields attributes Show fetch_fields attributes object
  
  field string Required
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  format string
  
  format string
  
  A custom format for date type runtime fields.
  
  input_field string
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  target_field string
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  target_index string
  
  script object
  
  Hide script attributes Show script attributes object
  
  source string | object
  
  One of:
  string-1 string SearchRequestBody object
  
  id string
  
  params object
  
  Specifies any named parameters that are passed into the script as variables. Use parameters instead of hard-coded values to decrease compile time.
  
  Hide params attribute Show params attribute object
  
  * object Additional properties
  
  lang string
  
  Any of:
  string-1 string string-2 string
  
  Values are painless, expression, mustache, or java.
  
  options object
  
  Hide options attribute Show options attribute object
  
  * string Additional properties
  
  type string Required
  
  Values are boolean, composite, date, double, geo_point, geo_shape, ip, keyword, long, or lookup.
  
  _source object
  
  Hide _source attributes Show _source attributes object
  
  includes array[string]
  
  An array of strings that defines the fields that will be excluded from the analysis. You do not need to add fields with unsupported data types to excludes, these fields are excluded from the analysis automatically.
  
  excludes array[string]
  
  An array of strings that defines the fields that will be included in the analysis.
  
  query object
  
  The Elasticsearch query domain-specific language (DSL). This value corresponds to the query object in an Elasticsearch search POST body. All the options that are supported by Elasticsearch can be used, as this object is passed verbatim to Elasticsearch. By default, this property has the following value: {"match_all": {}}.
  
  Query DSL
- version string Required

PUT /_ml/data_frame/analytics/{id}

PUT _ml/data_frame/analytics/model-flight-delays-pre
{
  "source": {
    "index": [
      "kibana_sample_data_flights"
    ],
    "query": {
      "range": {
        "DistanceKilometers": {
          "gt": 0
        }
      }
    },
    "_source": {
      "includes": [],
      "excludes": [
        "FlightDelay",
        "FlightDelayType"
      ]
    }
  },
  "dest": {
    "index": "df-flight-delays",
    "results_field": "ml-results"
  },
  "analysis": {
  "regression": {
    "dependent_variable": "FlightDelayMin",
    "training_percent": 90
    }
  },
  "analyzed_fields": {
    "includes": [],
    "excludes": [
      "FlightNum"
    ]
  },
  "model_memory_limit": "100mb"
}

resp = client.ml.put_data_frame_analytics(
    id="model-flight-delays-pre",
    source={
        "index": [
            "kibana_sample_data_flights"
        ],
        "query": {
            "range": {
                "DistanceKilometers": {
                    "gt": 0
                }
            }
        },
        "_source": {
            "includes": [],
            "excludes": [
                "FlightDelay",
                "FlightDelayType"
            ]
        }
    },
    dest={
        "index": "df-flight-delays",
        "results_field": "ml-results"
    },
    analysis={
        "regression": {
            "dependent_variable": "FlightDelayMin",
            "training_percent": 90
        }
    },
    analyzed_fields={
        "includes": [],
        "excludes": [
            "FlightNum"
        ]
    },
    model_memory_limit="100mb",
)

const response = await client.ml.putDataFrameAnalytics({
  id: "model-flight-delays-pre",
  source: {
    index: ["kibana_sample_data_flights"],
    query: {
      range: {
        DistanceKilometers: {
          gt: 0,
        },
      },
    },
    _source: {
      includes: [],
      excludes: ["FlightDelay", "FlightDelayType"],
    },
  },
  dest: {
    index: "df-flight-delays",
    results_field: "ml-results",
  },
  analysis: {
    regression: {
      dependent_variable: "FlightDelayMin",
      training_percent: 90,
    },
  },
  analyzed_fields: {
    includes: [],
    excludes: ["FlightNum"],
  },
  model_memory_limit: "100mb",
});

response = client.ml.put_data_frame_analytics(
  id: "model-flight-delays-pre",
  body: {
    "source": {
      "index": [
        "kibana_sample_data_flights"
      ],
      "query": {
        "range": {
          "DistanceKilometers": {
            "gt": 0
          }
        }
      },
      "_source": {
        "includes": [],
        "excludes": [
          "FlightDelay",
          "FlightDelayType"
        ]
      }
    },
    "dest": {
      "index": "df-flight-delays",
      "results_field": "ml-results"
    },
    "analysis": {
      "regression": {
        "dependent_variable": "FlightDelayMin",
        "training_percent": 90
      }
    },
    "analyzed_fields": {
      "includes": [],
      "excludes": [
        "FlightNum"
      ]
    },
    "model_memory_limit": "100mb"
  }
)

$resp = $client->ml()->putDataFrameAnalytics([
    "id" => "model-flight-delays-pre",
    "body" => [
        "source" => [
            "index" => array(
                "kibana_sample_data_flights",
            ),
            "query" => [
                "range" => [
                    "DistanceKilometers" => [
                        "gt" => 0,
                    ],
                ],
            ],
            "_source" => [
                "includes" => array(
                ),
                "excludes" => array(
                    "FlightDelay",
                    "FlightDelayType",
                ),
            ],
        ],
        "dest" => [
            "index" => "df-flight-delays",
            "results_field" => "ml-results",
        ],
        "analysis" => [
            "regression" => [
                "dependent_variable" => "FlightDelayMin",
                "training_percent" => 90,
            ],
        ],
        "analyzed_fields" => [
            "includes" => array(
            ),
            "excludes" => array(
                "FlightNum",
            ),
        ],
        "model_memory_limit" => "100mb",
    ],
]);

curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"source":{"index":["kibana_sample_data_flights"],"query":{"range":{"DistanceKilometers":{"gt":0}}},"_source":{"includes":[],"excludes":["FlightDelay","FlightDelayType"]}},"dest":{"index":"df-flight-delays","results_field":"ml-results"},"analysis":{"regression":{"dependent_variable":"FlightDelayMin","training_percent":90}},"analyzed_fields":{"includes":[],"excludes":["FlightNum"]},"model_memory_limit":"100mb"}' "$ELASTICSEARCH_URL/_ml/data_frame/analytics/model-flight-delays-pre"

client.ml().putDataFrameAnalytics(p -> p
    .analysis(a -> a
        .regression(r -> r
            .dependentVariable("FlightDelayMin")
            .trainingPercent("90")
        )
    )
    .analyzedFields(an -> an
        .excludes("FlightNum")
    )
    .dest(d -> d
        .index("df-flight-delays")
        .resultsField("ml-results")
    )
    .id("model-flight-delays-pre")
    .modelMemoryLimit("100mb")
    .source(s -> s
        .index("kibana_sample_data_flights")
        .query(q -> q
            .range(r -> r
                .untyped(u -> u
                    .field("DistanceKilometers")
                    .gt(JsonData.fromJson("0"))
                )
            )
        )
        .source(so -> so
            .excludes(List.of("FlightDelay","FlightDelayType"))
        )
    )
);

Request example

An example body for a `PUT _ml/data_frame/analytics/model-flight-delays-pre` request.

{
  "source": {
    "index": [
      "kibana_sample_data_flights"
    ],
    "query": {
      "range": {
        "DistanceKilometers": {
          "gt": 0
        }
      }
    },
    "_source": {
      "includes": [],
      "excludes": [
        "FlightDelay",
        "FlightDelayType"
      ]
    }
  },
  "dest": {
    "index": "df-flight-delays",
    "results_field": "ml-results"
  },
  "analysis": {
  "regression": {
    "dependent_variable": "FlightDelayMin",
    "training_percent": 90
    }
  },
  "analyzed_fields": {
    "includes": [],
    "excludes": [
      "FlightNum"
    ]
  },
  "model_memory_limit": "100mb"
}

Create a trained model Generally available

PUT /_ml/trained_models/{model_id}

Api key auth

Enable you to supply a trained model that is not created by data frame analytics.

Required authorization

Cluster privileges: manage_ml

Path parameters

model_id string Required

The unique identifier of the trained model.

Query parameters

defer_definition_decompression boolean Generally available

If set to true and a compressed_definition is provided, the request defers definition decompression and skips relevant validations.
wait_for_completion boolean Generally available

Whether to wait for all child operations (e.g. model download) to complete.

application/json

Body Required

compressed_definition string

The compressed (GZipped and Base64 encoded) inference definition of the model. If compressed_definition is specified, then definition cannot be specified.
definition object
Hide definition attributes Show definition attributes object
- preprocessors array[object]
  
  Collection of preprocessors
  Hide preprocessors attributes Show preprocessors attributes object
  
  frequency_encoding object
  
  Hide frequency_encoding attributes Show frequency_encoding attributes object
  
  field string Required
  
  feature_name string Required
  
  frequency_map object Required
  
  Hide frequency_map attribute Show frequency_map attribute object
  
  * number Additional properties
  
  one_hot_encoding object
  
  Hide one_hot_encoding attributes Show one_hot_encoding attributes object
  
  field string Required
  
  hot_map object Required
  
  Hide hot_map attribute Show hot_map attribute object
  
  * string Additional properties
  
  target_mean_encoding object
  
  Hide target_mean_encoding attributes Show target_mean_encoding attributes object
  
  field string Required
  
  feature_name string Required
  
  target_map object Required
  
  Hide target_map attribute Show target_map attribute object
  
  * number Additional properties
  
  default_value number Required
- trained_model object Required
  Hide trained_model attributes Show trained_model attributes object
  
  tree object
  
  Hide tree attributes Show tree attributes object
  
  classification_labels array[string]
  
  feature_names array[string] Required
  
  target_type string
  
  tree_structure array[object] Required
  
  Hide tree_structure attributes Show tree_structure attributes object
  
  decision_type string
  
  default_left boolean
  
  leaf_value number
  
  left_child number
  
  node_index number Required
  
  right_child number
  
  split_feature number
  
  split_gain number
  
  threshold number
  
  tree_node object
  
  Hide tree_node attributes Show tree_node attributes object
  
  decision_type string
  
  default_left boolean
  
  leaf_value number
  
  left_child number
  
  node_index number Required
  
  right_child number
  
  split_feature number
  
  split_gain number
  
  threshold number
  
  ensemble object
  
  Hide ensemble attributes Show ensemble attributes object
  
  aggregate_output object
  
  Hide aggregate_output attributes Show aggregate_output attributes object
  
  logistic_regression object
  
  Hide logistic_regression attribute Show logistic_regression attribute object
  
  weights number Required
  
  weighted_sum object
  
  Hide weighted_sum attribute Show weighted_sum attribute object
  
  weights number Required
  
  weighted_mode object
  
  Hide weighted_mode attribute Show weighted_mode attribute object
  
  weights number Required
  
  exponent object
  
  Hide exponent attribute Show exponent attribute object
  
  weights number Required
  
  classification_labels array[string]
  
  feature_names array[string]
  
  target_type string
  
  trained_models array[object] Required
description string

A human-readable description of the inference trained model.
inference_config object

Inference configuration provided when storing the model config
Hide inference_config attributes Show inference_config attributes object
- regression object
  Hide regression attributes Show regression attributes object
  
  results_field string
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  num_top_feature_importance_values number
  
  Specifies the maximum number of feature importance values per document.
  
  Default value is 0.
- classification object
  Hide classification attributes Show classification attributes object
  
  num_top_classes number
  
  Specifies the number of top class predictions to return. Defaults to 0.
  
  num_top_feature_importance_values number
  
  Specifies the maximum number of feature importance values per document.
  
  Default value is 0.
  
  prediction_field_type string
  
  Specifies the type of the predicted field to write. Acceptable values are: string, number, boolean. When boolean is provided 1.0 is transformed to true and 0.0 to false.
  
  results_field string
  
  The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
  
  top_classes_results_field string
  
  Specifies the field to which the top classes are written. Defaults to top_classes.
- text_classification object
  
  Text classification configuration options
  Hide text_classification attributes Show text_classification attributes object
  
  num_top_classes number
  
  Specifies the number of top class predictions to return. Defaults to 0.
  
  tokenization object
  
  Tokenization options stored in inference configuration
  
  Hide tokenization attributes Show tokenization attributes object
  
  bert object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert attributes Show bert attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  bert_ja object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert_ja attributes Show bert_ja attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  mpnet object
  
  BERT and MPNet tokenization configuration options
  
  Hide mpnet attributes Show mpnet attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  roberta object
  
  RoBERTa tokenization configuration options
  
  Hide roberta attributes Show roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  add_prefix_space boolean
  
  Should the tokenizer prefix input with a space character
  
  Default value is false.
  
  xlm_roberta object
  
  Hide xlm_roberta attributes Show xlm_roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  results_field string
  
  The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
  
  classification_labels array[string]
  
  Classification labels to apply other than the stored labels. Must have the same deminsions as the default configured labels
  
  vocabulary object
  
  Hide vocabulary attribute Show vocabulary attribute object
  
  index string Required
- zero_shot_classification object
  
  Zero shot classification configuration options
  Hide zero_shot_classification attributes Show zero_shot_classification attributes object
  
  tokenization object
  
  Tokenization options stored in inference configuration
  
  Hide tokenization attributes Show tokenization attributes object
  
  bert object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert attributes Show bert attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  bert_ja object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert_ja attributes Show bert_ja attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  mpnet object
  
  BERT and MPNet tokenization configuration options
  
  Hide mpnet attributes Show mpnet attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  roberta object
  
  RoBERTa tokenization configuration options
  
  Hide roberta attributes Show roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  add_prefix_space boolean
  
  Should the tokenizer prefix input with a space character
  
  Default value is false.
  
  xlm_roberta object
  
  Hide xlm_roberta attributes Show xlm_roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  hypothesis_template string
  
  Hypothesis template used when tokenizing labels for prediction
  
  Default value is "This example is {}.".
  
  classification_labels array[string] Required
  
  The zero shot classification labels indicating entailment, neutral, and contradiction Must contain exactly and only entailment, neutral, and contradiction
  
  results_field string
  
  The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
  
  multi_label boolean
  
  Indicates if more than one true label exists.
  
  Default value is false.
  
  labels array[string]
  
  The labels to predict.
- fill_mask object
  
  Fill mask inference options
  Hide fill_mask attributes Show fill_mask attributes object
  
  mask_token string
  
  The string/token which will be removed from incoming documents and replaced with the inference prediction(s). In a response, this field contains the mask token for the specified model/tokenizer. Each model and tokenizer has a predefined mask token which cannot be changed. Thus, it is recommended not to set this value in requests. However, if this field is present in a request, its value must match the predefined value for that model/tokenizer, otherwise the request will fail.
  
  num_top_classes number
  
  Specifies the number of top class predictions to return. Defaults to 0.
  
  tokenization object
  
  Tokenization options stored in inference configuration
  
  Hide tokenization attributes Show tokenization attributes object
  
  bert object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert attributes Show bert attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  bert_ja object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert_ja attributes Show bert_ja attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  mpnet object
  
  BERT and MPNet tokenization configuration options
  
  Hide mpnet attributes Show mpnet attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  roberta object
  
  RoBERTa tokenization configuration options
  
  Hide roberta attributes Show roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  add_prefix_space boolean
  
  Should the tokenizer prefix input with a space character
  
  Default value is false.
  
  xlm_roberta object
  
  Hide xlm_roberta attributes Show xlm_roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  results_field string
  
  The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
  
  vocabulary object Required
  
  Hide vocabulary attribute Show vocabulary attribute object
  
  index string Required
- learning_to_rank object
  Hide learning_to_rank attributes Show learning_to_rank attributes object
  
  default_params object
  
  Hide default_params attribute Show default_params attribute object
  
  * object Additional properties
  
  feature_extractors array[object]
  
  num_top_feature_importance_values number Required
- ner object
  
  Named entity recognition options
  Hide ner attributes Show ner attributes object
  
  tokenization object
  
  Tokenization options stored in inference configuration
  
  Hide tokenization attributes Show tokenization attributes object
  
  bert object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert attributes Show bert attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  bert_ja object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert_ja attributes Show bert_ja attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  mpnet object
  
  BERT and MPNet tokenization configuration options
  
  Hide mpnet attributes Show mpnet attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  roberta object
  
  RoBERTa tokenization configuration options
  
  Hide roberta attributes Show roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  add_prefix_space boolean
  
  Should the tokenizer prefix input with a space character
  
  Default value is false.
  
  xlm_roberta object
  
  Hide xlm_roberta attributes Show xlm_roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  results_field string
  
  The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
  
  classification_labels array[string]
  
  The token classification labels. Must be IOB formatted tags
  
  vocabulary object
  
  Hide vocabulary attribute Show vocabulary attribute object
  
  index string Required
- pass_through object
  
  Pass through configuration options
  Hide pass_through attributes Show pass_through attributes object
  
  tokenization object
  
  Tokenization options stored in inference configuration
  
  Hide tokenization attributes Show tokenization attributes object
  
  bert object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert attributes Show bert attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  bert_ja object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert_ja attributes Show bert_ja attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  mpnet object
  
  BERT and MPNet tokenization configuration options
  
  Hide mpnet attributes Show mpnet attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  roberta object
  
  RoBERTa tokenization configuration options
  
  Hide roberta attributes Show roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  add_prefix_space boolean
  
  Should the tokenizer prefix input with a space character
  
  Default value is false.
  
  xlm_roberta object
  
  Hide xlm_roberta attributes Show xlm_roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  results_field string
  
  The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
  
  vocabulary object
  
  Hide vocabulary attribute Show vocabulary attribute object
  
  index string Required
- text_embedding object
  
  Text embedding inference options
  Hide text_embedding attributes Show text_embedding attributes object
  
  embedding_size number
  
  The number of dimensions in the embedding output
  
  tokenization object
  
  Tokenization options stored in inference configuration
  
  Hide tokenization attributes Show tokenization attributes object
  
  bert object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert attributes Show bert attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  bert_ja object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert_ja attributes Show bert_ja attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  mpnet object
  
  BERT and MPNet tokenization configuration options
  
  Hide mpnet attributes Show mpnet attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  roberta object
  
  RoBERTa tokenization configuration options
  
  Hide roberta attributes Show roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  add_prefix_space boolean
  
  Should the tokenizer prefix input with a space character
  
  Default value is false.
  
  xlm_roberta object
  
  Hide xlm_roberta attributes Show xlm_roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  results_field string
  
  The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
  
  vocabulary object Required
  
  Hide vocabulary attribute Show vocabulary attribute object
  
  index string Required
- text_expansion object
  
  Text expansion inference options
  Hide text_expansion attributes Show text_expansion attributes object
  
  tokenization object
  
  Tokenization options stored in inference configuration
  
  Hide tokenization attributes Show tokenization attributes object
  
  bert object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert attributes Show bert attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  bert_ja object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert_ja attributes Show bert_ja attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  mpnet object
  
  BERT and MPNet tokenization configuration options
  
  Hide mpnet attributes Show mpnet attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  roberta object
  
  RoBERTa tokenization configuration options
  
  Hide roberta attributes Show roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  add_prefix_space boolean
  
  Should the tokenizer prefix input with a space character
  
  Default value is false.
  
  xlm_roberta object
  
  Hide xlm_roberta attributes Show xlm_roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  results_field string
  
  The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
  
  vocabulary object Required
  
  Hide vocabulary attribute Show vocabulary attribute object
  
  index string Required
- question_answering object
  
  Question answering inference options
  Hide question_answering attributes Show question_answering attributes object
  
  num_top_classes number
  
  Specifies the number of top class predictions to return. Defaults to 0.
  
  tokenization object
  
  Tokenization options stored in inference configuration
  
  Hide tokenization attributes Show tokenization attributes object
  
  bert object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert attributes Show bert attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  bert_ja object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert_ja attributes Show bert_ja attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  mpnet object
  
  BERT and MPNet tokenization configuration options
  
  Hide mpnet attributes Show mpnet attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  roberta object
  
  RoBERTa tokenization configuration options
  
  Hide roberta attributes Show roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  add_prefix_space boolean
  
  Should the tokenizer prefix input with a space character
  
  Default value is false.
  
  xlm_roberta object
  
  Hide xlm_roberta attributes Show xlm_roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  results_field string
  
  The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
  
  max_answer_length number
  
  The maximum answer length to consider
input object
Hide input attribute Show input attribute object
- field_names string | array[string] Required
metadata object

An object map that contains metadata about the model.
model_type string

Values are tree_ensemble, lang_ident, or pytorch.
model_size_bytes number

The estimated memory usage in bytes to keep the trained model in memory. This property is supported only if defer_definition_decompression is true or the model definition is not supplied.
platform_architecture string

The platform architecture (if applicable) of the trained mode. If the model only works on one platform, because it is heavily optimized for a particular processor architecture and OS combination, then this field specifies which. The format of the string must match the platform identifiers used by Elasticsearch, so one of, linux-x86_64, linux-aarch64, darwin-x86_64, darwin-aarch64, or windows-x86_64. For portable models (those that work independent of processor architecture or OS features), leave this field unset.
tags array[string]

An array of tags to organize the model.
prefix_strings object
Hide prefix_strings attributes Show prefix_strings attributes object
- ingest string
  
  String prepended to input at ingest
- search string
  
  String prepended to input at search

Responses

200 application/json
Hide response attributes Show response attributes object
- model_id string Required
- model_type string
  
  Values are tree_ensemble, lang_ident, or pytorch.
- tags array[string] Required
  
  A comma delimited string of tags. A trained model can have many tags, or none.
- version string
- compressed_definition string
- created_by string
  
  Information on the creator of the trained model.
- create_time string | number
  
  A date and time, either as a string whose format can depend on the context (defaulting to ISO 8601), or a number of milliseconds since the Epoch. Elasticsearch accepts both as input, but will generally output a string representation.
  
  One of:
  string-1 string UnitMillis number
- default_field_map object
  
  Any field map described in the inference configuration takes precedence.
  
  Hide default_field_map attribute Show default_field_map attribute object
  
  * string Additional properties
- description string
  
  The free-text description of the trained model.
- estimated_heap_memory_usage_bytes number
  
  The estimated heap usage in bytes to keep the trained model in memory.
- estimated_operations number
  
  The estimated number of operations to use the trained model.
- fully_defined boolean
  
  True if the full model definition is present.
- inference_config object
  
  Inference configuration provided when storing the model config
  
  Hide inference_config attributes Show inference_config attributes object
  
  regression object
  
  Hide regression attributes Show regression attributes object
  
  results_field string
  
  Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
  
  num_top_feature_importance_values number
  
  Specifies the maximum number of feature importance values per document.
  
  Default value is 0.
  
  classification object
  
  Hide classification attributes Show classification attributes object
  
  num_top_classes number
  
  Specifies the number of top class predictions to return. Defaults to 0.
  
  num_top_feature_importance_values number
  
  Specifies the maximum number of feature importance values per document.
  
  Default value is 0.
  
  prediction_field_type string
  
  Specifies the type of the predicted field to write. Acceptable values are: string, number, boolean. When boolean is provided 1.0 is transformed to true and 0.0 to false.
  
  results_field string
  
  The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
  
  top_classes_results_field string
  
  Specifies the field to which the top classes are written. Defaults to top_classes.
  
  text_classification object
  
  Text classification configuration options
  
  Hide text_classification attributes Show text_classification attributes object
  
  num_top_classes number
  
  Specifies the number of top class predictions to return. Defaults to 0.
  
  tokenization object
  
  Tokenization options stored in inference configuration
  
  Hide tokenization attributes Show tokenization attributes object
  
  bert object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert attributes Show bert attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  bert_ja object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert_ja attributes Show bert_ja attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  mpnet object
  
  BERT and MPNet tokenization configuration options
  
  Hide mpnet attributes Show mpnet attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  roberta object
  
  RoBERTa tokenization configuration options
  
  Hide roberta attributes Show roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  add_prefix_space boolean
  
  Should the tokenizer prefix input with a space character
  
  Default value is false.
  
  xlm_roberta object
  
  Hide xlm_roberta attributes Show xlm_roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  results_field string
  
  The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
  
  classification_labels array[string]
  
  Classification labels to apply other than the stored labels. Must have the same deminsions as the default configured labels
  
  vocabulary object
  
  Hide vocabulary attribute Show vocabulary attribute object
  
  index string Required
  
  zero_shot_classification object
  
  Zero shot classification configuration options
  
  Hide zero_shot_classification attributes Show zero_shot_classification attributes object
  
  tokenization object
  
  Tokenization options stored in inference configuration
  
  Hide tokenization attributes Show tokenization attributes object
  
  bert object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert attributes Show bert attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  bert_ja object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert_ja attributes Show bert_ja attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  mpnet object
  
  BERT and MPNet tokenization configuration options
  
  Hide mpnet attributes Show mpnet attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  roberta object
  
  RoBERTa tokenization configuration options
  
  Hide roberta attributes Show roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  add_prefix_space boolean
  
  Should the tokenizer prefix input with a space character
  
  Default value is false.
  
  xlm_roberta object
  
  Hide xlm_roberta attributes Show xlm_roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  hypothesis_template string
  
  Hypothesis template used when tokenizing labels for prediction
  
  Default value is "This example is {}.".
  
  classification_labels array[string] Required
  
  The zero shot classification labels indicating entailment, neutral, and contradiction Must contain exactly and only entailment, neutral, and contradiction
  
  results_field string
  
  The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
  
  multi_label boolean
  
  Indicates if more than one true label exists.
  
  Default value is false.
  
  labels array[string]
  
  The labels to predict.
  
  fill_mask object
  
  Fill mask inference options
  
  Hide fill_mask attributes Show fill_mask attributes object
  
  mask_token string
  
  The string/token which will be removed from incoming documents and replaced with the inference prediction(s). In a response, this field contains the mask token for the specified model/tokenizer. Each model and tokenizer has a predefined mask token which cannot be changed. Thus, it is recommended not to set this value in requests. However, if this field is present in a request, its value must match the predefined value for that model/tokenizer, otherwise the request will fail.
  
  num_top_classes number
  
  Specifies the number of top class predictions to return. Defaults to 0.
  
  tokenization object
  
  Tokenization options stored in inference configuration
  
  Hide tokenization attributes Show tokenization attributes object
  
  bert object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert attributes Show bert attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  bert_ja object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert_ja attributes Show bert_ja attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  mpnet object
  
  BERT and MPNet tokenization configuration options
  
  Hide mpnet attributes Show mpnet attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  roberta object
  
  RoBERTa tokenization configuration options
  
  Hide roberta attributes Show roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  add_prefix_space boolean
  
  Should the tokenizer prefix input with a space character
  
  Default value is false.
  
  xlm_roberta object
  
  Hide xlm_roberta attributes Show xlm_roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  results_field string
  
  The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
  
  vocabulary object Required
  
  Hide vocabulary attribute Show vocabulary attribute object
  
  index string Required
  
  learning_to_rank object
  
  Hide learning_to_rank attributes Show learning_to_rank attributes object
  
  default_params object
  
  Hide default_params attribute Show default_params attribute object
  
  * object Additional properties
  
  feature_extractors array[object]
  
  num_top_feature_importance_values number Required
  
  ner object
  
  Named entity recognition options
  
  Hide ner attributes Show ner attributes object
  
  tokenization object
  
  Tokenization options stored in inference configuration
  
  Hide tokenization attributes Show tokenization attributes object
  
  bert object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert attributes Show bert attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  bert_ja object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert_ja attributes Show bert_ja attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  mpnet object
  
  BERT and MPNet tokenization configuration options
  
  Hide mpnet attributes Show mpnet attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  roberta object
  
  RoBERTa tokenization configuration options
  
  Hide roberta attributes Show roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  add_prefix_space boolean
  
  Should the tokenizer prefix input with a space character
  
  Default value is false.
  
  xlm_roberta object
  
  Hide xlm_roberta attributes Show xlm_roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  results_field string
  
  The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
  
  classification_labels array[string]
  
  The token classification labels. Must be IOB formatted tags
  
  vocabulary object
  
  Hide vocabulary attribute Show vocabulary attribute object
  
  index string Required
  
  pass_through object
  
  Pass through configuration options
  
  Hide pass_through attributes Show pass_through attributes object
  
  tokenization object
  
  Tokenization options stored in inference configuration
  
  Hide tokenization attributes Show tokenization attributes object
  
  bert object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert attributes Show bert attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  bert_ja object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert_ja attributes Show bert_ja attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  mpnet object
  
  BERT and MPNet tokenization configuration options
  
  Hide mpnet attributes Show mpnet attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  roberta object
  
  RoBERTa tokenization configuration options
  
  Hide roberta attributes Show roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  add_prefix_space boolean
  
  Should the tokenizer prefix input with a space character
  
  Default value is false.
  
  xlm_roberta object
  
  Hide xlm_roberta attributes Show xlm_roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  results_field string
  
  The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
  
  vocabulary object
  
  Hide vocabulary attribute Show vocabulary attribute object
  
  index string Required
  
  text_embedding object
  
  Text embedding inference options
  
  Hide text_embedding attributes Show text_embedding attributes object
  
  embedding_size number
  
  The number of dimensions in the embedding output
  
  tokenization object
  
  Tokenization options stored in inference configuration
  
  Hide tokenization attributes Show tokenization attributes object
  
  bert object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert attributes Show bert attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  bert_ja object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert_ja attributes Show bert_ja attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  mpnet object
  
  BERT and MPNet tokenization configuration options
  
  Hide mpnet attributes Show mpnet attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  roberta object
  
  RoBERTa tokenization configuration options
  
  Hide roberta attributes Show roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  add_prefix_space boolean
  
  Should the tokenizer prefix input with a space character
  
  Default value is false.
  
  xlm_roberta object
  
  Hide xlm_roberta attributes Show xlm_roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  results_field string
  
  The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
  
  vocabulary object Required
  
  Hide vocabulary attribute Show vocabulary attribute object
  
  index string Required
  
  text_expansion object
  
  Text expansion inference options
  
  Hide text_expansion attributes Show text_expansion attributes object
  
  tokenization object
  
  Tokenization options stored in inference configuration
  
  Hide tokenization attributes Show tokenization attributes object
  
  bert object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert attributes Show bert attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  bert_ja object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert_ja attributes Show bert_ja attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  mpnet object
  
  BERT and MPNet tokenization configuration options
  
  Hide mpnet attributes Show mpnet attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  roberta object
  
  RoBERTa tokenization configuration options
  
  Hide roberta attributes Show roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  add_prefix_space boolean
  
  Should the tokenizer prefix input with a space character
  
  Default value is false.
  
  xlm_roberta object
  
  Hide xlm_roberta attributes Show xlm_roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  results_field string
  
  The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
  
  vocabulary object Required
  
  Hide vocabulary attribute Show vocabulary attribute object
  
  index string Required
  
  question_answering object
  
  Question answering inference options
  
  Hide question_answering attributes Show question_answering attributes object
  
  num_top_classes number
  
  Specifies the number of top class predictions to return. Defaults to 0.
  
  tokenization object
  
  Tokenization options stored in inference configuration
  
  Hide tokenization attributes Show tokenization attributes object
  
  bert object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert attributes Show bert attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  bert_ja object
  
  BERT and MPNet tokenization configuration options
  
  Hide bert_ja attributes Show bert_ja attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  mpnet object
  
  BERT and MPNet tokenization configuration options
  
  Hide mpnet attributes Show mpnet attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  roberta object
  
  RoBERTa tokenization configuration options
  
  Hide roberta attributes Show roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  add_prefix_space boolean
  
  Should the tokenizer prefix input with a space character
  
  Default value is false.
  
  xlm_roberta object
  
  Hide xlm_roberta attributes Show xlm_roberta attributes object
  
  do_lower_case boolean
  
  Should the tokenizer lower case the text
  
  Default value is false.
  
  max_sequence_length number
  
  Maximum input sequence length for the model
  
  Default value is 512.
  
  span number
  
  Tokenization spanning options. Special value of -1 indicates no spanning takes place
  
  Default value is -1.
  
  truncate string
  
  Values are first, second, or none.
  
  with_special_tokens boolean
  
  Is tokenization completed with special tokens
  
  Default value is true.
  
  results_field string
  
  The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
  
  max_answer_length number
  
  The maximum answer length to consider
- input object Required
  
  Hide input attribute Show input attribute object
  
  field_names array[string] Required
  
  An array of input field names for the model.
- license_level string
  
  The license level of the trained model.
- metadata object
  
  Hide metadata attributes Show metadata attributes object
  
  model_aliases array[string]
  
  feature_importance_baseline object
  
  An object that contains the baseline for feature importance values. For regression analysis, it is a single value. For classification analysis, there is a value for each class.
  
  Hide feature_importance_baseline attribute Show feature_importance_baseline attribute object
  
  * string Additional properties
  
  hyperparameters array[object]
  
  List of the available hyperparameters optimized during the fine_parameter_tuning phase as well as specified by the user.
  
  Hide hyperparameters attributes Show hyperparameters attributes object
  
  absolute_importance number
  
  A positive number showing how much the parameter influences the variation of the loss function. For hyperparameters with values that are not specified by the user but tuned during hyperparameter optimization.
  
  name string Required
  
  relative_importance number
  
  A number between 0 and 1 showing the proportion of influence on the variation of the loss function among all tuned hyperparameters. For hyperparameters with values that are not specified by the user but tuned during hyperparameter optimization.
  
  supplied boolean Required
  
  Indicates if the hyperparameter is specified by the user (true) or optimized (false).
  
  value number Required
  
  The value of the hyperparameter, either optimized or specified by the user.
  
  total_feature_importance array[object]
  
  An array of the total feature importance for each feature used from the training data set. This array of objects is returned if data frame analytics trained the model and the request includes total_feature_importance in the include request parameter.
  
  Hide total_feature_importance attributes Show total_feature_importance attributes object
  
  feature_name string Required
  
  importance array[object] Required
  
  A collection of feature importance statistics related to the training data set for this particular feature.
  
  Hide importance attributes Show importance attributes object
  
  mean_magnitude number Required
  
  The average magnitude of this feature across all the training data. This value is the average of the absolute values of the importance for this feature.
  
  max number Required
  
  The maximum importance value across all the training data for this feature.
  
  min number Required
  
  The minimum importance value across all the training data for this feature.
  
  classes array[object] Required
  
  If the trained model is a classification model, feature importance statistics are gathered per target class value.
  
  Hide classes attributes Show classes attributes object
  
  class_name string Required
  
  importance array[object] Required
  
  A collection of feature importance statistics related to the training data set for this particular feature.
- model_size_bytes number | string
  
  One of:
  number-1 number string-2 string
- model_package object
  
  Hide model_package attributes Show model_package attributes object
  
  create_time number
  
  Time unit for milliseconds
  
  description string
  
  inference_config object
  
  Hide inference_config attribute Show inference_config attribute object
  
  * object Additional properties
  
  metadata object
  
  Hide metadata attribute Show metadata attribute object
  
  * object Additional properties
  
  minimum_version string
  
  model_repository string
  
  model_type string
  
  packaged_model_id string Required
  
  platform_architecture string
  
  prefix_strings object
  
  Hide prefix_strings attributes Show prefix_strings attributes object
  
  ingest string
  
  String prepended to input at ingest
  
  search string
  
  String prepended to input at search
  
  size number | string
  
  One of:
  number-1 number string-2 string
  
  sha256 string
  
  tags array[string]
  
  vocabulary_file string
- location object
  
  Hide location attribute Show location attribute object
  
  index object Required
  
  Hide index attribute Show index attribute object
  
  name string Required
- platform_architecture string
- prefix_strings object
  
  Hide prefix_strings attributes Show prefix_strings attributes object
  
  ingest string
  
  String prepended to input at ingest
  
  search string
  
  String prepended to input at search

PUT /_ml/trained_models/{model_id}

curl \
 --request PUT 'http://api.example.com/_ml/trained_models/{model_id}' \
 --header "Authorization: $API_KEY" \
 --header "Content-Type: application/json" \
 --data '{"compressed_definition":"string","definition":{"preprocessors":[{"frequency_encoding":{"field":"string","feature_name":"string","frequency_map":{"additionalProperty1":42.0,"additionalProperty2":42.0}},"one_hot_encoding":{"field":"string","hot_map":{"additionalProperty1":"string","additionalProperty2":"string"}},"target_mean_encoding":{"field":"string","feature_name":"string","target_map":{"additionalProperty1":42.0,"additionalProperty2":42.0},"default_value":42.0}}],"trained_model":{"tree":{"classification_labels":["string"],"feature_names":["string"],"target_type":"string","tree_structure":[{"decision_type":"string","default_left":true,"leaf_value":42.0,"left_child":42.0,"node_index":42.0,"right_child":42.0,"split_feature":42.0,"split_gain":42.0,"threshold":42.0}]},"tree_node":{"decision_type":"string","default_left":true,"leaf_value":42.0,"left_child":42.0,"node_index":42.0,"right_child":42.0,"split_feature":42.0,"split_gain":42.0,"threshold":42.0},"ensemble":{"aggregate_output":{"logistic_regression":{"weights":42.0},"weighted_sum":{"weights":42.0},"weighted_mode":{"weights":42.0},"exponent":{"weights":42.0}},"classification_labels":["string"],"feature_names":["string"],"target_type":"string","trained_models":[{}]}}},"description":"string","inference_config":{"regression":{"results_field":"string","num_top_feature_importance_values":0},"classification":{"num_top_classes":42.0,"num_top_feature_importance_values":0,"prediction_field_type":"string","results_field":"string","top_classes_results_field":"string"},"text_classification":{"num_top_classes":42.0,"tokenization":{"bert":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"bert_ja":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"mpnet":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"roberta":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true,"add_prefix_space":false},"xlm_roberta":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true}},"results_field":"string","classification_labels":["string"],"vocabulary":{"index":"string"}},"zero_shot_classification":{"tokenization":{"bert":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"bert_ja":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"mpnet":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"roberta":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true,"add_prefix_space":false},"xlm_roberta":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true}},"hypothesis_template":"\"This example is {}.\"","classification_labels":["string"],"results_field":"string","multi_label":false,"labels":["string"]},"fill_mask":{"mask_token":"string","num_top_classes":42.0,"tokenization":{"bert":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"bert_ja":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"mpnet":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"roberta":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true,"add_prefix_space":false},"xlm_roberta":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true}},"results_field":"string","vocabulary":{"index":"string"}},"learning_to_rank":{"default_params":{"additionalProperty1":{},"additionalProperty2":{}},"feature_extractors":[{}],"num_top_feature_importance_values":42.0},"ner":{"tokenization":{"bert":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"bert_ja":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"mpnet":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"roberta":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true,"add_prefix_space":false},"xlm_roberta":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true}},"results_field":"string","classification_labels":["string"],"vocabulary":{"index":"string"}},"pass_through":{"tokenization":{"bert":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"bert_ja":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"mpnet":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"roberta":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true,"add_prefix_space":false},"xlm_roberta":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true}},"results_field":"string","vocabulary":{"index":"string"}},"text_embedding":{"embedding_size":42.0,"tokenization":{"bert":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"bert_ja":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"mpnet":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"roberta":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true,"add_prefix_space":false},"xlm_roberta":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true}},"results_field":"string","vocabulary":{"index":"string"}},"text_expansion":{"tokenization":{"bert":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"bert_ja":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"mpnet":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"roberta":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true,"add_prefix_space":false},"xlm_roberta":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true}},"results_field":"string","vocabulary":{"index":"string"}},"question_answering":{"num_top_classes":42.0,"tokenization":{"bert":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"bert_ja":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"mpnet":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true},"roberta":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true,"add_prefix_space":false},"xlm_roberta":{"do_lower_case":false,"max_sequence_length":512,"span":-1,"truncate":"first","with_special_tokens":true}},"results_field":"string","max_answer_length":42.0}},"input":{"field_names":"string"},"metadata":{},"model_type":"tree_ensemble","model_size_bytes":42.0,"platform_architecture":"string","tags":["string"],"prefix_strings":{"ingest":"string","search":"string"}}'

Delete a query ruleset Generally available

DELETE /_query_rules/{ruleset_id}

Api key auth

Remove a query ruleset and its associated data. This is a destructive action that is not recoverable.

Required authorization

Cluster privileges: manage_search_query_rules

Path parameters

ruleset_id string Required

The unique identifier of the query ruleset to delete

Responses

200 application/json
Hide response attribute Show response attribute object
- acknowledged boolean Required
  
  For a successful response, this value is always true. On failure, an exception is returned instead.

DELETE /_query_rules/{ruleset_id}

DELETE _query_rules/my-ruleset/

resp = client.query_rules.delete_ruleset(
    ruleset_id="my-ruleset",
)

const response = await client.queryRules.deleteRuleset({
  ruleset_id: "my-ruleset",
});

response = client.query_rules.delete_ruleset(
  ruleset_id: "my-ruleset"
)

$resp = $client->queryRules()->deleteRuleset([
    "ruleset_id" => "my-ruleset",
]);

curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_query_rules/my-ruleset/"

client.queryRules().deleteRuleset(d -> d
    .rulesetId("my-ruleset")
);

Behavioral analytics

Compact and aligned text (CAT)

Get CAT help Generally available

Responses

Get datafeeds Generally available

Required authorization

Path parameters

Query parameters

Responses

Get all connectors Beta

Query parameters

Responses

default_value number | string | boolean | null Required

tooltip string | null

error string | null

index_name string | null

last_access_control_sync_scheduled_at string | number

last_incremental_sync_scheduled_at string | number

last_seen string | number

last_sync_scheduled_at string | number

last_synced string | number

Create a connector Beta

Body

Responses

Update the connector configuration Beta

Path parameters

Body Required

default_value number | string | boolean | null Required

value number | string | boolean | null Required

value number | string | boolean | null Required

tooltip string | null

Responses

Update the connector filtering Beta

Path parameters

Body Required

created_at string | number

updated_at string | number

created_at string | number

updated_at string | number

created_at string | number

updated_at string | number

created_at string | number

updated_at string | number

Responses

Update the connector pipeline Beta

Path parameters

Body Required

Responses

Update the connector service type Beta

Path parameters

Body Required

Responses

Update the connector status Technical preview

Path parameters

Body Required

Responses

Update data stream lifecycles Generally available

Path parameters

Query parameters

Body

Responses

Get EQL search results Generally available

Path parameters

Query parameters

Body Required

filter object | array[object]

fields object | array[object]

source string | object

lang string

Responses

reason string | null

Index

Delete indices Generally available

Required authorization

Path parameters

Query parameters

Responses

Check indices Generally available

Path parameters

Query parameters