Update connector pipeline API

This functionality is in beta and is subject to change. The design and code is less mature than official GA features and is being provided as-is with no warranties. Beta features are not subject to the support SLA of official GA features.

Updates the pipeline configuration of a connector.

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

To get started with Connector APIs, check out our tutorial.

Request

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PUT _connector/<connector_id>/_pipeline

Prerequisites

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To sync data using self-managed connectors, you need to deploy the Elastic connector service. on your own infrastructure. This service runs automatically on Elastic Cloud for Elastic managed connectors.
The connector_id parameter should reference an existing connector.

Path parameters

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<connector_id>: (Required, string)

Request body

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pipeline: (Required, object) The pipeline configuration of the connector. The pipeline determines how data is processed during ingestion into Elasticsearch.

Pipeline configuration must include the following attributes:

extract_binary_content (Required, boolean) A flag indicating whether to extract binary content during ingestion.
name (Required, string) The name of the ingest pipeline.
reduce_whitespace (Required, boolean) A flag indicating whether to reduce extra whitespace in the ingested content.
run_ml_inference (Required, boolean) A flag indicating whether to run machine learning inference on the ingested content.

Response codes

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200: Connector pipeline field was successfully updated.
400: The connector_id was not provided or the request payload was malformed.
404 (Missing resources): No connector matching connector_id could be found.

Examples

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The following example updates the pipeline property for the connector with ID my-connector:

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
    },
)
print(resp)

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
    }
  }
)
puts response

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,
  },
});
console.log(response);

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

{
    "result": "updated"
}

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