Mapping data source fields - Amazon Kendra
Using Amazon Kendra reserved or common document fields

Mapping data source fields

Amazon Kendra data source connectors can map document or content fields from your data source to fields in your Amazon Kendra index. By default, each connector is designed to crawl specific data source fields. Default data source fields and their properties cannot be changed or customized. On the Amazon Kendra console, default fields and default field properties that cannnot be edited are grayed out.

Amazon Kendra connectors also allow you to map custom document or content fields from your data source to custom fields in your index. For example, if you have a field in your data source called "dept" that contains department information for a document, you can map it to an index field called "Department". That way, you can use the field when querying documents.

You can also map Amazon Kendra reserved or common fields such as _created_at. If your data source has a field called "creation_date", you can map this to the equivalent Amazon Kendra reserved field called _created_at. For more information on Amazon Kendra reserved fields, see Document attributes or fields.

You can map fields for most data sources. You can create field mappings for the following data sources:

  • Adobe Experience Manager

  • Alfresco

  • Aurora (MySQL)

  • Aurora (PostgreSQL)

  • Amazon FSx (Windows)

  • Amazon FSx (NetApp ONTAP)

  • Amazon RDS/Aurora

  • Amazon RDS (Microsoft SQL Server)

  • Amazon RDS (MySQL)

  • Amazon RDS (Oracle)

  • Amazon RDS (PostgreSQL)

  • Amazon Kendra Web Crawler

  • Amazon WorkDocs

  • Box

  • Confluence

  • Dropbox

  • Drupal

  • GitHub

  • Google Workspace Drives

  • Gmail

  • IBM DB2

  • Jira

  • Microsoft Exchange

  • Microsoft OneDrive

  • Microsoft SharePoint

  • Microsoft Teams

  • Microsoft SQL Server

  • Microsoft Yammer

  • MySQL

  • Oracle Database

  • PostgreSQL

  • Quip

  • Salesforce

  • ServiceNow

  • Slack

  • Zendesk

If you store your documents in an S3 bucket, or S3 data source, you specify your fields using a JSON metadata file. For more information, see S3 data source connector.

Mapping your data source fields to an index field is a three-step process:

  1. Create an index. For more information, see Creating an index.

  2. Update the index to add fields.

  3. Create a data source and include field mappings to map reserved fields and any custom fields to Amazon Kendra index fields.

To update the index to add custom fields, use the console to edit the data source field mappings and add a custom field or use the UpdateIndex API. You can add a total of 500 custom fields to your index.

For database data sources, if the name of the database column matches the name of a reserved field, the field and column are automatically mapped.

With the UpdateIndex API, you add reserved and custom fields using DocumentMetadataConfigurationUpdates.

The following JSON example uses DocumentMetadataConfigurationUpdates to add a field called "Department" to the index.

"DocumentmetadataConfigurationUpdates": [
   {
       "Name": "Department",
       "Type": "STRING_VALUE"
   }
]

When you create the field, you have the option of setting how the field is used for search. You can choose from the following:

  • Displayable—Determines whether the field is returned in the query response. The default is true.

  • Facetable—Indicates that the field can be used to create facets. The default is false.

  • Searchable—Determines whether the field is used in the search. The default is true for string fields and false for number and date fields.

  • Sortable—Indicates that the field can be used to sort the response from a query. Can only be set for date, number, and string fields. Can't be set for string list fields.

The following JSON example uses DocumentMetadataConfigurationUpdates to add a field called "Department" to the index and marks it as facetable.

"DocumentMetadataConfigurationUpdates": [
   {
       "Name": "Department",
       "Type": "STRING_VALUE",
       "Search": {
           "Facetable": true
       }
   }
]

Using Amazon Kendra reserved or common document fields

With the UpdateIndex API, you can create reserved or common fields using DocumentMetadataConfigurationUpdates and specifying the Amazon Kendra reserved index field name to map to your equivalent document attribute/field name. You can also create custom fields. If you use a data source connector, most include field mappings that map your data source document fields to Amazon Kendra index fields. If you use the console, you update fields by selecting your data source, selecting the edit action, and then proceeding next to the field mappings section for configuring the data source.

You can configure the Search object to set a field as either displayable, facetable, searchable, and sortable. You can configure the Relevance object to set a field's rank order, boost duration or time period to apply to boosting, freshness, importance value, and importance values mapped to specific field values. If you use the console, you can set the search settings for a field by selecting the facet option in the navigation menu. To set relevance tuning, select the option to search your index in the navigation menu, enter a query, and use the side panel options to tune the search relevance. You cannot change the field type once you have created the field.

Amazon Kendra has the following reserved or common document fields that you can use:

  • _authors—A list of one or more authors responsible for the content of the document.

  • _category—A category that places a document in a specific group.

  • _created_at—The date and time in ISO 8601 format that the document was created. For example, 2012-03-25T12:30:10+01:00 is the ISO 8601 date-time format for March 25th 2012 at 12:30PM (plus 10 seconds) in Central European Time.

  • _data_source_id—The identifier of the data source that contains the document.

  • _document_body—The content of the document.

  • _document_id—A unique identifier for the document.

  • _document_title—The title of the document.

  • _excerpt_page_number—The page number in a PDF file where the document excerpt appears. If your index was created before September 8, 2020, you must re-index your documents before you can use this attribute.

  • _faq_id—If this is a question-answer type document (FAQ), a unique identifier for the FAQ.

  • _file_type—The file type of the document, such as pdf or doc.

  • _last_updated_at—The date and time in ISO 8601 format that the document was last updated. For example, 2012-03-25T12:30:10+01:00 is the ISO 8601 date-time format for March 25th 2012 at 12:30PM (plus 10 seconds) in Central European Time.

  • _source_uri—The URI where the document is available. For example, the URI of the document on a company website.

  • _version—An identifier for the specific version of a document.

  • _view_count—The number of times that the document has been viewed.

  • _language_code (String)—The code for a language that applies to the document. This defaults to English if you do not specify a language. For more information on supported languages, including their codes, see Adding documents in languages other than English.

For custom fields, you create these fields using DocumentMetadataConfigurationUpdates with the UpdateIndex API, just as you do when creating a reserved or common field. You must set the appropriate data type for your custom field. If you use the console, you update fields by selecting your data source, selecting the edit action, and then proceeding next to the field mappings section for configuring the data source. Some data sources don't support adding new fields or custom fields. You cannot change the field type once you have created the field.

The following are the types you can set for custom fields:

  • Date

  • Number

  • String

  • String list

If you added documents to the index using BatchPutDocument API, Attributes lists the fields/attributes of your documents and you create fields using the DocumentAttribute object.

For documents indexed from an Amazon S3 data source, you create fields using a JSON metadata file that includes the fields information.

If you use a supported database as your data source, you can configure your fields using the field mappings option.