Research agents for LifeSciences with Amazon Bedrock Agents

Explore the following components in the repository:

Agents catalog

Library of specialized agents for different R&D workflows agent-catalog

Multi-agent collaboration

Framework for agent collaboration and knowledge sharing. End to end example for cancer biomarker discovery multi-agent

Evaluation

Methods for assessing agent performance and result quality. End to end example for cancer biomarker discovery evaluations

Accelerate analysis and discovery of cancer biomarkers with Agents for Bedrock

Read more about these agents here: https://aws.amazon.com/blogs/machine-learning/accelerate-analysis-and-discovery-of-cancer-biomarkers-with-amazon-bedrock-agents/

Solution Overview

The multi-agent solution overview is illustrated below.

Deployment

Note: You can choose to deploy the agents with one-click deployment or set them up yourself in workshop mode

Important

Access to Amazon Bedrock foundation models (not granted by default). To gain access, follow the official documentation.

1. Upload the Infra_cfn.yaml file from the amazon-bedrock-agents-cancer-biomarker-discovery repository to AWS CloudFormation. This template will set up:

Warning

Launching this stack will create 2 VPCs (Infrastructure and UI).

• Networking infrastructure (VPC, Subnets, etc.)
• Amazon Redshift database
• Bedrock Agent with Actions
• Knowledgebase
• Streamlit UI frontend

2. Deploy the Infra_cfn.yaml template:
   • Default parameter values can remain unchanged
   • Parameter descriptions:
     • BedrockModelId: ID of the Foundation Model for the Agent (permissions scoped to Anthropic Claude 3 Sonnet model)
     • EnvironmentName: Differentiates the application if launched in the same AWS account (lowercase, one number, max 5 characters)
     • RedshiftDatabaseName: Name for the Redshift database
     • RedshiftUserName: Username for Redshift database login
     • RedshiftPassword: Password for Redshift database login
     • GithubLink: Default repository for the Agent (do not change)
     • ImageTag: Tag of the Docker image for Streamlit UI deployment

Note

Full deployment takes approximately 10-15 minutes. Stack can also be launched in us-east-1 or us-west-2 by clicking launch stack below

Region	Infra_cfn.yaml
us-east-1
us-west-2

3. After stack launch is successful manually sync the Knowledgebase:

   1. Navigate to the Bedrock dashboard via AWS Console search
   2. Click the option icon (top left) to open the navigation bar
   3. Select "Knowledge bases" under the "Builder tools" tab
   4. Choose the Knowledgebase created by the CloudFormation template
   5. Scroll to the "Data Source" option box
   6. Select the data source (radio button) and click "Sync"

4. Access the UI:

   1. Navigate to AWS CloudFormation via AWS Console search
   2. Click the Streamlit nested stack (format: <stackname>-StreamlitBuildNestedStack-<123ABCXXXX>)
   3. In the Outputs tab, find the CloudFrontURL link and add 'https://' to the beginning of the URL and paste in your browser

Contributing Guidelines

Setting Up Your Development Environment

1. Fork the repository to your GitHub account. Ensure the fork remains public during development and testing.

2. Clone your forked repository to your local machine.

3. Update the GitHub URL in the following configuration files to point to your forked repository:

   • infra_cfn.yaml
   • agent_build.yaml
   • streamlit_build.yaml

4. For testing purposes, deploy the infra_cfn.yaml template to AWS CloudFormation.

Submitting a Pull Request

**Follow the guidelines to contribute a new agent to the catalog here: add-a-new-agent

1. Ensure you have forked the main repository: amazon-bedrock-agents-healthcare-lifesciences

2. Create a new branch in your forked repository for your changes.

3. Implement your changes, following the project's coding standards and guidelines.

4. Commit your changes with clear, concise commit messages.

5. Push your branch to your forked repository on GitHub.

6. Open a pull request from your branch to the main repository's main branch.

7. Provide a clear description of your changes in the pull request, including any relevant issue numbers.

8. Be prepared to address any feedback or questions during the code review process.

License

This project is licensed under the MIT License.

Legal Notes

Important: This solution is for demonstrative purposes only. It is not for clinical use and is not a substitute for professional medical advice, diagnosis, or treatment. The associated notebooks, including the trained model and sample data, are not intended for production. It is each customers’ responsibility to determine whether they are subject to HIPAA, and if so, how best to comply with HIPAA and its implementing regulations. Before using AWS in connection with protected health information, customers must enter an AWS Business Associate Addendum (BAA) and follow its configuration requirements.