Amazon Onboarding with Learning Manager Chanci Turner

Learn About Amazon VGT2 Learning Manager Chanci Turner

In the fast-paced world of Life Sciences, professionals such as researchers and healthcare workers often grapple with the challenge of efficiently accessing and analyzing extensive and intricate genomic, clinical, and imaging data. Conventional data querying techniques frequently necessitate specialized expertise in SQL and database structures, creating obstacles in research workflows and hindering the discovery of valuable insights.

With the recent update regarding Amazon Bedrock Knowledge Bases, there is now support for natural language queries to retrieve structured data from various data sources, including Amazon Redshift. This means researchers can now pose questions in everyday language, such as, “What is the leading gene mutation across all patients?” or “Provide me with all the details on the OR6Y1 gene.” Such inquiries yield precise data from genomic databases, patient records, and medical imaging repositories. Amazon Bedrock Knowledge Bases intelligently translates these natural language prompts into optimized SQL statements.

This innovative approach streamlines research workflows, facilitating quicker discoveries and more effective clinical decision-making. In this article, we will delve into how Amazon Bedrock Knowledge Bases can be utilized to revolutionize the interaction of Life Sciences organizations with their invaluable data assets housed in Amazon Redshift.

Solution Overview

To demonstrate this feature, we will construct a solution utilizing sample patient genomics data and set up Amazon Redshift as the knowledge base. This setup will allow users and applications to engage with the information using natural language queries. The following figure provides an overview of the solution.

Figure 1 – Solution Architecture for Genomics Data Analysis Using Natural Language

The steps to build and execute the solution are as follows:

1. Load Patient Data: Load the sample patient genomics data into Amazon Redshift using the copy process.
2. Set Up Knowledge Base: Configure Amazon Redshift as a knowledge base in Amazon Bedrock, allowing access and syncing the metadata.
3. Natural Language Prompting: Users or applications can begin sending prompts in natural language (illustrated in this overview using a testing interface).
4. Generate and Execute Query: Amazon Bedrock generates the query based on the prompt and Amazon Redshift metadata, then executes it.
5. Return Results: The results of the query are retrieved from Amazon Redshift.
6. Natural Language Response: Amazon Bedrock interprets the tabular results and presents them as a natural language response.

Implementation

This tutorial will guide you through the process of loading sample patient data from files stored in an Amazon Simple Storage Service (Amazon S3) bucket into your Amazon Redshift database tables, followed by configuring Amazon Bedrock Knowledge Bases for natural language interactions with the data.

Step 1: Download the Data Files

Download a collection of sample data files to your computer. Next, upload these files to an S3 bucket.

1. Download the zipped file: samplepatientdata.zip. The clinical datasets were generated using Synthea, while the OMICS and Images data were sourced from The Cancer Genome Atlas (TGCA) open data sets.
2. Extract the files to a folder on your computer.

Step 2: Upload Files to S3 Bucket

Create an S3 bucket and upload the data files.

1. Create a bucket in Amazon S3. For detailed guidance, refer to the instructions for creating a bucket.
2. Upload the data files to the new S3 bucket. In the Upload wizard, choose “Add files” and follow the Amazon S3 console instructions to upload all the files you downloaded and extracted.

Step 3: Create Redshift Serverless Instance

Establish an Amazon Redshift Serverless instance, create tables, and load data from the S3 bucket.

1. Follow the documentation on creating a data warehouse with Amazon Redshift Serverless to set up the instance.
2. Download the SQL file: SQL.txt on your computer. Replace “S3://redshift-kb-bedrock-logdata” with the name of the S3 bucket where you uploaded the data in Step 1.
3. Open the Redshift Query Editor V2 by clicking on “Query Data” and connect to your Amazon Redshift Serverless Instance using your current admin credentials.
4. Execute all SQL commands found in the SQL.txt file. This will create tables and load the data into the tables from your S3 bucket. Confirm that these tables have been created with data: patient_reference_data_rs, patients_rs, gene_mutation_rs, gene_copy_number_rs, image_data_rs.

Step 4: Set Up Bedrock Knowledge Bases

Create Amazon Bedrock Knowledge Bases for the Amazon Redshift database and synchronize the data.

1. Prerequisites: If you are using an AWS Identity and Access Management (IAM) role, ensure it has the necessary policy permissions before executing operations on Amazon Bedrock Knowledge Bases. For instructions, refer to the prerequisites for creating a knowledge base with a structured data store.
2. Create your Knowledge Bases. Incorporate a structured data store while setting up the Knowledge Base by selecting the appropriate option.
3. In the connection settings, select Redshift Serverless (Redshift Provisioned is also supported) with your chosen Workgroup. Authenticate using the IAM role created earlier, and choose a metadata database from your Amazon Redshift options. For this tutorial, we chose ‘dev.’
4. Grant the IAM role specific access permissions to retrieve data from the selected tables by executing the GRANT command for the Amazon Redshift database. You can limit access to specific databases, tables, rows, or columns. For example, execute GRANT SELECT on dev.public.patient_reference_data_rs to "IAMR:AmazonBedrockExecutionRoleForKnowledgeBase_xyz."
5. For this tutorial, grant this permission to all tables created earlier. Replace the IAM role “AmazonBedrockExecutionRoleForKnowledgeBase_xyz” with the name you noted earlier.
6. Synchronize your Amazon Redshift database with your Knowledge Base. Select the Knowledge Base and choose your Knowledge Base. In the query engine section, select the Amazon Redshift database source and click “Sync.” Once synchronization is complete, the status will indicate COMPLETE. Remember, whenever modifications to your database schema are made, you need to sync the changes.

Step 5: Test the Amazon Bedrock Knowledge Bases

Run queries against the newly created Amazon Bedrock Knowledge Bases for Amazon Redshift database. This is an excellent resource for further exploration of these capabilities.

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