IMPORTANT: No additional bug fixes or documentation updates will be released for this version. For the latest information, see the current release documentation.

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Using the Attachment Processor in a Pipeline

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Using the Attachment Processor in a Pipeline

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Table 1. Attachment options

Name	Required	Default	Description
`field`	yes	-	The field to get the base64 encoded field from
`target_field`	no	attachment	The field that will hold the attachment information
`indexed_chars`	no	100000	The number of chars being used for extraction to prevent huge fields. Use `-1` for no limit.
`indexed_chars_field`	no	`null`	Field name from which you can overwrite the number of chars being used for extraction. See `indexed_chars`.
`properties`	no	all properties	Array of properties to select to be stored. Can be `content`, `title`, `name`, `author`, `keywords`, `date`, `content_type`, `content_length`, `language`
`ignore_missing`	no	`false`	If `true` and `field` does not exist, the processor quietly exits without modifying the document

For example, this:

PUT _ingest/pipeline/attachment
{
  "description" : "Extract attachment information",
  "processors" : [
    {
      "attachment" : {
        "field" : "data"
      }
    }
  ]
}
PUT my_index/_doc/my_id?pipeline=attachment
{
  "data": "e1xydGYxXGFuc2kNCkxvcmVtIGlwc3VtIGRvbG9yIHNpdCBhbWV0DQpccGFyIH0="
}
GET my_index/_doc/my_id

Returns this:

{
  "found": true,
  "_index": "my_index",
  "_type": "_doc",
  "_id": "my_id",
  "_version": 1,
  "_seq_no": 22,
  "_primary_term": 1,
  "_source": {
    "data": "e1xydGYxXGFuc2kNCkxvcmVtIGlwc3VtIGRvbG9yIHNpdCBhbWV0DQpccGFyIH0=",
    "attachment": {
      "content_type": "application/rtf",
      "language": "ro",
      "content": "Lorem ipsum dolor sit amet",
      "content_length": 28
    }
  }
}

To specify only some fields to be extracted:

PUT _ingest/pipeline/attachment
{
  "description" : "Extract attachment information",
  "processors" : [
    {
      "attachment" : {
        "field" : "data",
        "properties": [ "content", "title" ]
      }
    }
  ]
}

Extracting contents from binary data is a resource intensive operation and consumes a lot of resources. It is highly recommended to run pipelines using this processor in a dedicated ingest node.

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