Enrich your content and metadata to enhance your search experience with custom document enrichment in Amazon Kendra

Amazon Kendra consumers can now enhance file metadata and material throughout the file ingestion procedure utilizing customized document enrichment (CDE). Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra reimagines search for your sites and applications so your staff members and customers can quickly find the content theyre searching for, even when its scattered throughout several places and content repositories within your organization.
You can even more improve the precision and search experience of Amazon Kendra by enhancing the quality of documents indexed in it. Documents with exact material and abundant metadata are more searchable and yield more precise outcomes. Organizations frequently have big repositories of raw documents that can be improved for search by customizing material or including metadata before indexing. So how does CDE assist? By streamlining the process of creating, customizing, or erasing file metadata and content prior to theyre consumed into Amazon Kendra. This can consist of detecting entities from text, extracting text from images, transcribing audio and video, and more by producing custom logic or using services like Amazon Comprehend, Amazon Textract, Amazon Transcribe, Amazon Rekognition, and others.
In this post, we show you how to use CDE in Amazon Kendra using custom-made logic or with AWS services like Amazon Textract, Amazon Transcribe, and Amazon Comprehend. We demonstrate CDE utilizing basic examples and offer a step-by-step guide for you to experience CDE in an Amazon Kendra index in your own AWS account.
CDE introduction
CDE allows you to develop, modify, or delete file metadata and content when you ingest your documents into Amazon Kendra. Lets understand the Amazon Kendra file ingestion workflow in the context of CDE.
The following diagram shows the CDE workflow.

The path a file takes depends on the presence of various CDE elements:

Course taken when no CDE exists– Steps 1 and 2

Course taken with only CDE basic operations– Steps 3, 4, and 2

Course taken with only CDE advanced operations– Steps 6, 7, 8, and 9

Course taken when CDE standard operations and advanced operations are present– Steps, 3, 5, 7, 8, and 9

The CDE standard operations and advanced operations elements are optional. For more details on the CDE basic operations and advanced operations with the preExtraction and postExtraction AWS Lambda functions, describe the Custom Document Enrichment area in the Amazon Kendra Developer Guide.
In this post, we walk you through four usage cases:

Instantly appoint category characteristics based upon the subdirectory of the file being ingested
Immediately extract text while ingesting scanned image files to make them searchable
Immediately produce a transcription while ingesting audio and video files to make them searchable
Automatically produce elements based on entities in a document to boost the search experience

Requirements
You can follow the detailed guide in your AWS account to get a first-hand experience of utilizing CDE. Before getting going, finish the following requirements:

About the Authors.
Abhinav Jawadekar is a Senior Partner Solutions Architect at Amazon Web Services. Abhinav deals with AWS Partners to help them in their cloud journey.

CloudFormation stack.
Amazon Kendra index.
S3 bucket.

Select link in the answer to begin the video.

The following screenshot shows the faceted search engine result.

Submit these to the S3 bucket being used as the information source in the folder s3://<< YOUR-DATASOURCE-BUCKET>>/ Data/Media/.
Open the information source on the Amazon Kendra console start an information source sync.
When the data source sync is complete, browse to Search indexed content and get in the inquiry Where is Yosemite National Park?.

Go into the conditions, ARN, and container details for the pre-extraction and post-extraction functions.
For Service permissions, choose Enter custom-made role ARN and go into the CDERoleARN value (available on the stacks Outputs tab).

If you look for to an offset of 84.44 seconds (1 minute, 24 seconds), youll hear exactly what the excerpt reveals.
Automatically generate facets based on entities in a document to improve the search experience.
Appropriate elements such as the entities in files like places, individuals, and events, when provided as part of search engine result, provide an interactive method for a user to filter search results page and discover what theyre trying to find. Amazon Kendra metadata, when inhabited correctly, can supply these aspects, and improves the user experience.
The post-extraction Lambda function allows you to carry out the reasoning to process the text drawn out by Amazon Kendra from the ingested file, then produce and upgrade the metadata. The post-extraction function we set up implements the code to invoke Amazon Comprehend to detect entities from the text extracted by Amazon Kendra, and uses them to update the document metadata, which exists as facets in an Amazon Kendra search. The function code is embedded in the CloudFormation design template we utilized previously. You can pick the Template tab of the stack on the CloudFormation console and review the code for PostExtractionLambda.
The maximum runtime permitted a CDE post-extraction function is 60 seconds, so you can just use it to implement tasks that can be finished in that time.
Prior to we can experiment with this example, we require to specify the entity types that we discover utilizing Amazon Comprehend as aspects in our Amazon Kendra index.

On the Amazon Kendra console, select Document enrichments in the navigation pane.
Select the CDE we configured.
On the Actions menu, pick Edit.
Select Add standard operations.

For each of the documents that were consumed, the category attribute worths set by the CDE standard operations are seen as selectable facets.
Keep in mind Document fields for each of the results. When you click on it, it reveals the fields or characteristics of the file included in that result as seen in the screenshot listed below.

In this action, you need the ARNs of the preExtraction and postExtraction functions (offered on the Outputs tab of the CloudFormation stack). We utilize the exact same bucket that youre utilizing as the information source pail.

All the elements of the type LOCATION, organization, and individual are instantly produced by the post-extraction Lambda function with the detected entities utilizing Amazon Comprehend. You can use these aspects to interactively filter the search results page. You can also attempt a few more inquiries and try out the aspects.
Tidy up.
After you have actually explore the Amazon Kendra index and the features of CDE, delete the infrastructure you provisioned in your AWS account while working on the examples in this post:.

The following screenshot shows the search engine result.

We utilize CDE basic operations to instantly set the classification characteristic based upon the subdirectory a document comes from while the document is being consumed.

Configure the S3 bucket as a data source utilizing the S3 information source connector in the Amazon Kendra index you developed. When setting up the data source, in the Additional configurations section, define the Include pattern to be Data/. For more details and directions, refer to the Using Amazon Kendra S3 Connector subsection of the Ingesting Documents section in the Amazon Kendra Essentials workshop and Getting Started with an Amazon S3 data source (console).
Extract the contents of the data file AWS_Whitepapers. zip to your regional device and publish them to the S3 pail you developed at the course s3://<< YOUR-DATASOURCE-BUCKET>>/ Data/ while preserving the subdirectory structure.

Pick Next.

The data source sync can use up to 10– 15 minutes to complete.

While waiting on the information source sync to finish, choose Facet meaning in the navigation pane.
For the Index field of _ category, choose Facetable, Searchable, and Displayable to make it possible for these homes.
Choose Save.
Search back to the information source page and wait on the sync to finish.
When the data source sync is total, pick Search indexed content in the navigation pane.
Get in the inquiry Which service supplies 11 nines of sturdiness?.
After you get the search engine result, choose Filter search results page.

Review all the information and pick Add document enrichment.
Search back to the data source were using by selecting Data sources in the navigation pane and select the data source.
Select Sync now to begin information source sync.

Include 2 more operations: one for Media and one for GEN_META.

Conclusion.
Enhancing information and metadata can improve the efficiency of search engine result and improve the search experience. You can use the custom information enrichment (CDE) function of Amazon Kendra to easily automate the CDE procedure by producing, customizing, or deleting the metadata using the fundamental operations. You can likewise utilize the sophisticated operations with pre-extraction and post-extraction Lambda functions to implement the logic to manipulate the information and metadata.
We showed utilizing subdirectories to appoint classifications, utilizing Amazon Textract to extract text from scanned images, using Amazon Transcribe to produce a records of audio and video files, and using Amazon Comprehend to spot entities that are added as metadata and later readily available as elements to communicate with the search results page. This is just an illustration of how you can use CDE to produce a distinguished search experience for your users.
For a deeper dive into what you can achieve by integrating other AWS services with Amazon Kendra, refer to Make your audio and video files searchable utilizing Amazon Transcribe and Amazon Kendra, Build a smart search option with automatic material enrichment, and other posts on the Amazon Kendra blog site.

Offer a distinct name for your CloudFormation stack and the name of the container you just produced as a specification.
Choose Next, select the acknowledgement check boxes, and select Create stack.
After the stack production is total, keep in mind the contents of the Outputs. We use these values later on.

Publish this file to the S3 container being utilized as the data source in the folder s3://<< YOUR-DATASOURCE-BUCKET>>/ Data/Media/.
On the Amazon Kendra console, open the information source and begin a data source sync.
When the information source sync is total, browse to Search indexed content and get in the query What is the procedure to configure VPN over AWS Direct Connect?.

From the selectable aspects, you can pick a category, such as Best Practices, to filter your search engine result to be only from the Best Practices classification, as displayed in the following screenshot. The search experience improved considerably without requiring extra manual actions throughout document intake.

Choose Next.
Leave the setup for both Lambda functions blank.
For Service permissions, choose Enter custom function ARN and get in the CDERoleARN worth (readily available on the stacks Outputs tab).

Choose the link from the top search result.

The scanned image appears, as in the following screenshot.

Immediately extract text while consuming scanned image documents to make them searchable
In order for files that are scanned as images to be searchable, you initially need to draw out the text from such documents and ingest that text in an Amazon Kendra index. The pre-extraction Lambda function from the CDE advanced operations provides a place to carry out text extraction and modification reasoning. The pre-extraction function we configure has the code to extract the text from images utilizing Amazon Textract. The function code is embedded in the CloudFormation template we utilized earlier. You can choose the Template tab of the design template on the AWS CloudFormation console and evaluate the code for PreExtractionLambda.
We now set up CDE advanced operations to try out this and additional examples.

Choose Next..

Pick Next.

You can explore comparable concerns associated with Yellowstone.
Instantly produce a transcription while ingesting audio or video files to make them searchable.
Similar to images, audio and video material requires to be transcribed in order to be searchable. The pre-extraction Lambda function likewise includes the code to call Amazon Transcribe for audio and video files to transcribe them and extract a time-marked records. Lets try it out.
The maximum runtime allowed for a CDE pre-extraction Lambda function is 5 minutes (300 seconds), so you can just utilize it to transcribe audio or video files of brief period, about 10 minutes or less. For longer files, you can use the method explained in Make your audio and video files searchable using Amazon Transcribe and Amazon Kendra.
The sample information file Media.zip consists of a video file How_do_I_configure_a_VPN_over_AWS_Direct_Connect _. mp4, which has a video tutorial.

Amazon Kendra consumers can now improve document metadata and content during the document intake procedure utilizing custom-made document enrichment (CDE). This can include spotting entities from text, extracting text from images, transcribing audio and video, and more by developing customized reasoning or utilizing services like Amazon Comprehend, Amazon Textract, Amazon Transcribe, Amazon Rekognition, and others.
For more info and guidelines, refer to the Using Amazon Kendra S3 Connector subsection of the Ingesting Documents area in the Amazon Kendra Essentials workshop and Getting Started with an Amazon S3 information source (console).
In order for documents that are scanned as images to be searchable, you first need to extract the text from such files and ingest that text in an Amazon Kendra index. The post-extraction function we configured carries out the code to invoke Amazon Comprehend to identify entities from the text extracted by Amazon Kendra, and uses them to upgrade the document metadata, which is presented as facets in an Amazon Kendra search.

Automatically appoint classification attributes based on the subdirectory of the file being ingested
The files in the sample data are kept in subdirectories Best_Practices, Databases, General, Machine_Learning, Security, and Well_Architected. The S3 pail used as the information source appears like the following screenshot.

You can see all the fundamental operations you added.

Choose Add file enrichment.

Download the sample information files AWS_Whitepapers. zip, GenMeta.zip, and Media.zip to a regional drive on your computer.
In your AWS account, develop a brand-new Amazon Kendra index, Developer Edition. To learn more and instructions, describe the Getting Started chapter in the Amazon Kendra Essentials workshop and Creating an index.
Open the AWS Management Console, and make certain that youre logged in to your AWS account
Develop an Amazon Simple Storage Service (Amazon S3) container to utilize as a data source. Describe Amazon S3 User Guide to learn more.
Click on to introduce the AWS CloudFormation to release the preExtraction and postExtraction Lambda functions and the needed AWS Identity and Access Management (IAM) roles. It will open the AWS CloudFormation Management Console.

The following screenshot reveals the search engine result.

The following screenshot reveals the outcomes.

On the Amazon Kendra console, open the index you developed.
Choose Data sources in the navigation pane.
Select the data source utilized in this example.
Copy the data source ID.
Choose Document enrichment in the navigation pane.
Pick Add document enrichment.
For Data Source ID, enter the ID you copied.
Enter six basic operations, one representing each subdirectory.

Extract the contents of the GenMeta.zip data file and submit the files United_Nations_Climate_Change_conference_Wikipedia.
Open the information source on the Amazon Kendra console and start a data source sync.
When the information source sync is complete, browse to Search indexed content and get in the query What is Paris contract?.
After you get the results, choose Filter search results in the navigation pane.

Now were all set to ingest scanned images into our index. The sample data file Media.zip you downloaded earlier consists of 2 image files: Yosemite.png and Yellowstone.png. These are scanned images of the Wikipedia pages of Yosemite National Park and Yellowstone National Park, respectively.

On the Amazon Kendra console, choose the index were working on.
Select Facet meaning in the navigation pane.
Choose Add field and add fields for COMMERCIAL_ITEM, DATE, EVENT, LOCATION, ORGANIZATION, OTHER, PERSON, QUANTITY, and TITLE of type StringList.
Make LOCATION, ORGANIZATION and PERSON facetable by selecting Facetable.

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