Connect to Microsoft SharePoint for your Amazon Bedrock knowledge base

Sign in to the AWS Management Console using an IAM role with Amazon Bedrock permissions, and open the Amazon Bedrock console at https://console.aws.amazon.com/bedrock/.

From the left navigation pane, select Knowledge bases.

In the Knowledge bases section, select Create knowledge base.

Provide the knowledge base details.

1. Provide the knowledge base name and optional description.

2. Provide the AWS Identity and Access Management role for the necessary access permissions required to create a knowledge base.

   Note

   The IAM role with all the required permissions can be created for you as part of the console steps for creating a knowledge base. After you have completed the steps for creating a knowledge base, the IAM role with all the required permissions are applied to your specific knowledge base.

3. Create any tags you want to assign to your knowledge base.

Go to the next section to configure your data source.

Choose SharePoint as your data source and provide the connection configuration details.

1. Provide the data source name and optional description.

2. Provide your SharePoint site URL/URLs. For example, for SharePoint Online, https://yourdomain.sharepoint.com/sites/mysite. Your URL must start with https and contain sharepoint.com. Your site URL must be the actual SharePoint site, not sharepoint.com/ or sites/mysite/home.aspx

3. Provide the domain name of your SharePoint instance.

Check the advanced settings. You can optionally change the default selected settings.

Set your transient data encryption key and data deletion policy in the advanced settings.

For KMS key settings, you can choose either a custom key or use the default provided data encryption key.

While converting your data into embeddings, Amazon Bedrock encrypts your transient data with a key that AWS owns and manages, by default. You can use your own KMS key. For more information, see Encryption of transient data storage during data ingestion.

For data deletion policy settings, you can choose either:

Continue configuring your data source.

Provide the authentication information to connect to your SharePoint instance:

1. For OAuth 2.0 authentication, provide the tenant ID. You can find your tenant ID in the Properties of your Azure Active Directory portal or in your OAuth application.

2. For OAuth 2.0 authentication, go to AWS Secrets Manager to add your secret authentication credentials or use an existing Amazon Resource Name (ARN) for the secret you created. Your secret must contain the SharePoint admin username and password, and your registered app client ID and client secret. For an example application, see Register a client application in Microsoft Entra ID (formerly known as Azure Active Directory) on the Microsoft Learn website.

Continue configuring your data source.

Choose to use filters/regular expressions patterns to include or exclude certain content. All standard content is crawled otherwise.

Continue configuring your data source.

Choose either the default or customized chunking and parsing configurations.

1. If you choose custom settings, select one of the following chunking options:

   • Fixed-size chunking: Content split into chunks of text of your set approximate token size. You can set the maximum number of tokens that must not exceed for a chunk and the overlap percentage between consecutive chunks.

   • Default chunking: Content split into chunks of text of up to 300 tokens. If a single document or piece of content contains less than 300 tokens, the document is not further split.

   • Hierarchical chunking: Content organized into nested structures of parent-child chunks. You set the maximum parent chunk token size and the maximum child chunk token size. You also set the absolute number of overlap tokens between consecutive parent chunks and consecutive child chunks.

   • Semantic chunking: Content organized into semantically similar text chunks or groups of sentences. You set the maximum number of sentences surrounding the target/current sentence to group together (buffer size). You also set the breakpoint percentile threshold for dividing the text into meaningful chunks. Semantic chunking uses a foundation model. View Amazon Bedrock pricing for information on the cost of foundation models.

   • No chunking: Each document is treated as a single text chunk. You might want to pre-process your documents by splitting them into separate files.

   Note

   You can’t change the chunking strategy after you have created the data source.

2. You can choose to use Amazon Bedrock’s foundation model for parsing documents to parse more than standard text. You can parse tabular data within documents with their structure intact, for example. View Amazon Bedrock pricing for information on the cost of foundation models.

3. You can choose to use an AWS Lambda function to customize your chunking strategy and how your document metadata attributes/fields are treated and ingested. Provide the Amazon S3 bucket location for the Lambda function input and output.

Go to the next section to configure your vector store.

Choose a model for converting your data into vector embeddings.

Create a vector store to allow Amazon Bedrock to store, update, and manage embeddings. You can quick create a new vector store or select from a supported vector store you have created. Currently, only Amazon OpenSearch Serverless vector store is available to use with this data source. If you create a new vector store, an Amazon OpenSearch Serverless vector search collection and index with the required fields is set up for you. If you select from a supported vector store, you must map the vector field names and metadata field names.

Go to the next section to review your knowledge base configurations.

Check the details of your knowledge base. You can edit any section before going ahead and creating your knowledge base.