Amazon Kendra is a highly accurate and simple-to-use intelligent search service powered by machine learning (ML). Amazon Kendra offers a suite of data source connectors to simplify the process of ingesting and indexing your content, wherever it resides.
Valuable data in organizations is stored in both structured and unstructured repositories. An enterprise search solution should be able to pull together data across several structured and unstructured repositories to index and search on.
One such unstructured data repository is Microsoft Exchange. Email conversations contain important messages exchanged between various parties over time. Users often attach documents containing valuable information in the context of that email. In addition to emails, an Exchange account gives access to other valuable sources of information like calendar entries, OneNote notebooks, and contacts.
We’re excited to announce that you can now use the Amazon Kendra connector for Microsoft Exchange to search information stored in your Exchange account. In this post, we show how to index information stored in Exchange and use the Amazon Kendra intelligent search function. In addition, the ML-powered intelligent search can accurately find information from unstructured documents having natural language narrative content, for which keyword search is not very effective.
With Amazon Kendra, you can configure multiple data sources to provide a central place to search across your document repository. For our solution, we demonstrate how to index a Exchange repository or folder using the Amazon Kendra connector for Exchange. The solution consists of the following steps:
To try out the Amazon Kendra connector for Exchange, you need the following:
Before we set up the Exchange data source, we need a few details about your Exchange repository. Let’s gather those in advance.
This is where you can add or remove admin permissions.
To store your Exchange credentials in Secrets Manager, compete the following steps:
To configure the Amazon Kendra connector, complete the following steps:
This creates and propagates the IAM role and then creates the Amazon Kendra index, which can take up to 30 minutes.
Complete the following steps to create your data source:
We have kept the default selections, but you can fine-tune your selection of content as needed.
Now that you have ingested the content from your Exchange account into your Amazon Kendra index, you can test some queries.
The Exchange connector also crawls local identity information from Exchange. You can use this feature to narrow down your query by user.
For Microsoft Exchange, we don’t import groups, we just import user names. User names are email IDs in this case.
This brings you a filtered set of results based on your criteria.
When fronting Amazon Kendra with an application such as an application built using Experience Builder, you can pass the user identity (in the form of the email ID) to Amazon Kendra to ensure that each user only sees content specific to their user ID. Alternately, you can use AWS IAM Identity Center (successor to AWS Single Sign-On) to control user context being passed to Amazon Kendra to limit queries by user.
Congratulations! You have successfully used Amazon Kendra to surface answers and insights based on the content indexed from your Exchange account.
This solution has the following limitations:
To avoid incurring future costs, clean up the resources you created as part of this solution. If you created a new Amazon Kendra index while testing this solution, delete it. If you only added a new data source using the Amazon Kendra connector for Exchange, delete that data source.
With the Microsoft Exchange connector for Amazon Kendra, organizations can tap into the repository of information stored in their account securely using intelligent search powered by Amazon Kendra.
To learn about these possibilities and more, refer to the Amazon Kendra Developer Guide. For more information on how you can create, modify, or delete metadata and content when ingesting your data from Exchange, refer to Enriching your documents during ingestion and Enrich your content and metadata to enhance your search experience with custom document enrichment in Amazon Kendra.
Ashish Lagwankar is a Senior Enterprise Solutions Architect at AWS. His core interests include AI/ML, serverless, and container technologies. Ashish is based in the Boston, MA, area and enjoys reading, outdoors, and spending time with his family.