Create an Amazon Bedrock Knowledge Base component

To create a Knowledge Base

1. Navigate to the Amazon SageMaker Unified Studio landing page by using the URL from your administrator.

2. Access Amazon SageMaker Unified Studio using your IAM or single sign-on (SSO) credentials. For more information, see Access Amazon SageMaker Unified Studio.

3. Choose the Build menu at the top of the page.

4. In the MACHINE LEARNING & GENERATIVE AI section, choose My apps.

5. In the Select or create a new project to continue dialog box, select the project that you want to use.

6. In the left pane, choose Asset gallery.

7. Choose My components.

8. In the Components section, choose Create component and then Knowledge Base. The Create Knowledge Base pane is shown.

9. For Name, enter a name for the Knowledge Base.

10. For Description, enter a description for the Knowledge Base.

11. In Add data sources, do one of the following:

    • Use a document as a data source by doing the following:

      1. Choose Local file.

      2. Choose Click to upload and upload the document that you want the Knowledge Base to use. Alternatively, add your source documents by dragging and dropping the document from your computer.

      For more information, see Document data source.

    • Use a web crawler as a data source by doing the following:

      1. Choose Web crawler.

      2. Provide the Source URLs of the URLs you want to crawl. You can add up to 9 additional URLs by selecting Add Source URLs. By providing a source URL, you are confirming that you are authorized to crawl its domain.

      3. (Optional) Choose Specify web crawler configs to make the following optional configuration changes:

         • Website domain range. Set the domain that you want the Knowledge Base to crawl. For more information, see Website domain range for crawling URLs.

         • Maximum throttling of crawling speed. Set the speed at which the Knowledge Base crawls through the source URLs. For more information, see Throttle crawling speed.

         • URL regex filter. Set regex filters for including (Include patterns) or excluding Exclude patterns URLS from the web crawl. For more information, see Use a URL regex filter to include or exclude URLs.

         • Choose Back to leave the web crawler configuration pane.

12. In Configurations, under Data storage and processing, do the following:

    1. For Embeddings model, select a foundation model from the drop down to use for transforming your data into vector embeddings.

    2. For Embedding type and Vector dimensions, select an option from the dropdown to optimize accuracy, cost, and latency. Your options for embedding types and vector dimensions may be limited depending on the embeddings model that you chose.

       Note

       Amazon OpenSearch Serverless is the only vector store that supports binary vector embeddings. Floating-point vector embeddings are supported by all available vector stores.

    3. For Vector store choose from one of the following options:

       • Vector engine for Amazon OpenSearch Serverless ‐ Provides contextually relevant responses across billions of vectors in milliseconds. Supports searches combined with text-based keywords for hybrid requests.

       • Amazon S3 vectors ‐ Optimizes cost-effectiveness, durability, and latency for storage of large, long-term vector data sets. Amazon S3 vector buckets do not support web crawler data sources.

       • Amazon Neptune Analytics (GraphRAG) ‐ Provides high-performance graph analytics and graph-based Retrieval Augmented Generation (GraphRAG) solutions. You must have access to Claude 3 Haiku in order to use this vector store. Contact your administrator if you do not have the necessary permissions.

       Note

       Support for Amazon S3 vectors is in preview release for Amazon Bedrock in SageMaker Unified Studio and is subject to change.

       Once you select an option for your vector store, Amazon Bedrock in SageMaker Unified Studio will create the vector store on your behalf.

    4. For Chunking strategy, choose either Default, Fixed sized, Hierarchical, Semantic, or None. These options represent different methods for breaking down data into smaller segments before embedding.

    5. For Parsing strategy, choose either Bedrock default parser or Foundation model as a parser. If you choose Foundation model as a parser, do the following:

       1. For Choose a foundation model for parsing select your preferred foundation model. You can only choose models that your administrator has enabled for parsing. If you don't see a suitable model, contact your administrator.

       2. (Optional) Overwrite the Instructions for the parser to suit your specific needs.

13. Choose Create to create the Knowledge Base.

14. Use the Knowledge Base in an app, by doing one of the following:

    • If your app is a chat agent app, do Add an Amazon Bedrock Knowledge Base component to a chat agent app.

    • If your app is a flow app, do Add a Knowledge Base component to a flow app.