Perform AI prompt-chaining with Amazon Bedrock
This sample project demonstrates how you can integrate with Amazon Bedrock to perform AI prompt-chaining and build high-quality chatbots using Amazon Bedrock. The project chains together some prompts and resolves them in the sequence in which they're provided. Chaining of these prompts augments the ability of the language model being used to deliver a highly-curated response.
This sample project creates the state machine, the supporting AWS resources, and configures the related IAM permissions. Explore this sample project to learn about using Amazon Bedrock optimized service integration with Step Functions state machines, or use it as a starting point for your own projects.
Prerequisites
This sample project uses the Cohere Command large language model (LLM). To successfully run this sample project, you must add access to this LLM from the Amazon Bedrock console. To add the model access, do the following:
Open theAmazon Bedrock console.
On the navigation pane, chooseModel access.
ChooseManage model access.
Select the check box next toCohere.
ChooseRequest access. TheAccess status forCohere model shows asAccess granted.
Step 1: Create the state machine
Open theStep Functions console and chooseCreate state machine.
ChooseCreate from template and find the related starter template. ChooseNext to continue.
Choose how to use the template:
Run a demo – creates a read-only state machine. After review, you can create the workflow and all related resources.
Build on it – provides an editable workflow definition that you can review, customize, and deploy with your own resources. (Related resources, such as functions or queues, willnot be created automatically.)
ChooseUse template to continue with your selection.
Standard charges apply for services deployed to your account.
Step 2: Run the demo state machine
If you chose theRun a demo option, all related resources will be deployed and ready to run. If you chose theBuild on it option, you might need to set placeholder values and create additional resources before you can run your custom workflow.
ChooseDeploy and run.
Wait for the CloudFormation stack to deploy. This can take up to 10 minutes.
After theStart execution option appears, review theInput and chooseStart execution.
Congratulations!
You should now have a running demo of your state machine. You can choose states in theGraph view to review input, output, variables, definition, and events.
