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Ingest-attachment plugin
The ingest-attachment
plugin enables OpenSearch to extract content and other information from files using the Apache text extraction library Tika. Supported document formats include PPT, PDF, RTF, ODF, and many more Tika (Supported Document Formats).
The input field must be a base64-encoded binary.
Installing the plugin
Install the ingest-attachment
plugin using the following command:
./bin/opensearch-plugin install ingest-attachment
Attachment processor options
Name | Required | Default | Description |
---|---|---|---|
field | Yes | N/A | The field from which to get the base64-encoded binary. |
target_field | No | Attachment | The field that stores the attachment information. |
properties | No | All properties | An array of properties that should be stored. Can be content , language , date , title , author , keywords , content_type , or content_length . |
indexed_chars | No | 100_000 | The number of characters used for extraction to prevent fields from becoming too large. Use -1 for no limit. |
indexed_chars_field | No | null | The field name used to overwrite the number of chars being used for extraction, for example, indexed_chars . |
ignore_missing | No | false | When true , the processor exits without modifying the document when the specified field doesn’t exist. |
Example
The following steps show you how to get started with the ingest-attachment
plugin.
Step 1: Create an index for storing your attachments
The following command creates an index for storing your attachments:
PUT /example-attachment-index
{
"mappings": {
"properties": {}
}
}
Step 2: Create a pipeline
The following command creates a pipeline containing the attachment processor:
PUT _ingest/pipeline/attachment
{
"description" : "Extract attachment information",
"processors" : [
{
"attachment" : {
"field" : "data"
}
}
]
}
Step 3: Store an attachment
Convert the attachment to a base64 string to pass it as data
. In this example the base64
command converts the file lorem.rtf
:
base64 lorem.rtf
Alternatively, you can use Node.js to read the file to base64
, as shown in the following commands:
import * as fs from "node:fs/promises";
import path from "node:path";
const filePath = path.join(import.meta.dirname, "lorem.rtf");
const base64File = await fs.readFile(filePath, { encoding: "base64" });
console.log(base64File);
The.rtf
file contains the following base64 text:
Lorem ipsum dolor sit amet
: e1xydGYxXGFuc2kNCkxvcmVtIGlwc3VtIGRvbG9yIHNpdCBhbWV0DQpccGFyIH0=
.
PUT example-attachment-index/_doc/lorem_rtf?pipeline=attachment
{
"data": "e1xydGYxXGFuc2kNCkxvcmVtIGlwc3VtIGRvbG9yIHNpdCBhbWV0DQpccGFyIH0="
}
Query results
With the attachment processed, you can now search through the data using search queries, as shown in the following example:
POST example-attachment-index/_search
{
"query": {
"match": {
"attachment.content": "ipsum"
}
}
}
OpenSearch responds with the following:
{
"took": 5,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 1,
"relation": "eq"
},
"max_score": 1.1724279,
"hits": [
{
"_index": "example-attachment-index",
"_id": "lorem_rtf",
"_score": 1.1724279,
"_source": {
"data": "e1xydGYxXGFuc2kNCkxvcmVtIGlwc3VtIGRvbG9yIHNpdCBhbWV0DQpccGFyIH0=",
"attachment": {
"content_type": "application/rtf",
"language": "pt",
"content": "Lorem ipsum dolor sit amet",
"content_length": 28
}
}
}
]
}
}
Extracted information
The following fields can be extracted using the plugin:
content
language
date
title
author
keywords
content_type
content_length
To extract only a subset of these fields, define them in the properties
of the pipeline processor, as shown in the following example:
PUT _ingest/pipeline/attachment
{
"description" : "Extract attachment information",
"processors" : [
{
"attachment" : {
"field" : "data",
"properties": ["content", "title", "author"]
}
}
]
}
Limit the extracted content
To prevent extracting too many characters and overloading the node memory, the default limit is 100_000
. You can change this value using the setting indexed_chars
. For example, you can use -1
for unlimited characters, but you need to make sure you have enough HEAP space on your OpenSearch node to extract the content of large documents.
You can also define this limit per document using the indexed_chars_field
request field. If a document contains indexed_chars_field
, it will overwrite the indexed_chars
setting, as shown in the following example:
PUT _ingest/pipeline/attachment
{
"description" : "Extract attachment information",
"processors" : [
{
"attachment" : {
"field" : "data",
"indexed_chars" : 10,
"indexed_chars_field" : "max_chars",
}
}
]
}
With the attachment pipeline configured, you can extract the default 10
characters without specifying max_chars
in the request, as shown in the following example:
PUT example-attachment-index/_doc/lorem_rtf?pipeline=attachment
{
"data": "e1xydGYxXGFuc2kNCkxvcmVtIGlwc3VtIGRvbG9yIHNpdCBhbWV0DQpccGFyIH0="
}
Alternatively, you can change the max_char
per document in order to extract up to 15
characters, as shown in the following example:
PUT example-attachment-index/_doc/lorem_rtf?pipeline=attachment
{
"data": "e1xydGYxXGFuc2kNCkxvcmVtIGlwc3VtIGRvbG9yIHNpdCBhbWV0DQpccGFyIH0=",
"max_chars": 15
}