Amazon Bedrock Flows is a feature within Amazon Bedrock that enables developers to orchestrate complex AI workflows efficiently through a visual interface. It allows for the seamless integration of foundation models, prompts, agents, knowledge bases, guardrails, and other AWS services, facilitating the rapid development and deployment of generative AI applications.
Prerequisites
Before creating a flow in Amazon Bedrock, ensure the following:
- AWS Management Console Access: You should have access to the AWS Management Console with appropriate permissions to use Amazon Bedrock services.
- IAM Roles and Permissions: Set up the necessary IAM roles with permissions to create and manage flows. If you plan to use a custom service role, ensure it has the appropriate permissions.
- Resource Preparation: Depending on the nodes you plan to include in your flow, you may need to prepare resources such as prompts, knowledge bases, agents, AWS Lambda functions, or Amazon Lex bots.
Key Features of Amazon Bedrock Flows
- Visual Workflow Builder: Provides an intuitive drag-and-drop interface to design and connect various components, streamlining the creation of AI workflows without the need for extensive coding.
- Integration with AWS Services: Enables seamless connectivity with AWS services such as AWS Lambda, allowing for the incorporation of custom business logic and functionalities into AI workflows.
- Enhanced Safety with Guardrails: Incorporates Amazon Bedrock Guardrails to filter out harmful content and unwanted topics, ensuring the generation of safe and compliant outputs.
- Traceability Features: Offers detailed tracing capabilities that provide visibility into workflow execution, facilitating efficient debugging and validation of AI workflows.
How to Create a Basic Application Using Flow Builder
Let’s create a basic customer service application using Amazon Bedrock’s Flow Builder. This application will analyze user input to determine the sentiment expressed—positive, negative, or neutral—and provide an appropriate response.
1. Create a New Flow:
- In the Amazon Bedrock console, navigate to the Flows section.
- Click on “Create Flow” and provide a name (e.g., “sentiment_analysis_flow”) and a description.
- Specify a service role. You can let Amazon Bedrock create a new service role or select an existing one.
2. Add and Configure Nodes:
- In the Flow Builder, you’ll see a “Flow input” node and a “Flow output” node.
- From the Nodes pane, drag a “Prompt” node into the canvas.
- Configure the Prompt node with the following prompt:
The following is a text: "{{input}}"
Determine the sentiment of the text and categorize it as one of the following:
Positive, Negative, Neutral.- Connect the “Flow input” node to the “Prompt” node.
3. Implement Conditional Logic:
- Add a “Condition” node to the canvas.
- Configure the Condition node to evaluate the output from the Prompt node:
- If the output contains “Positive,” route to a node that handles positive sentiments.
- If the output contains “Negative,” route to a node that handles negative sentiments.
- If the output contains “Neutral,” route to a node that handles neutral sentiments.
- Connect the output of the Prompt node to the input of the Condition node.
4. Handle Different Sentiment Paths:
- For Positive Sentiment:
- Add a node that processes positive sentiment responses, such as logging positive feedback or triggering a positive acknowledgment.
- For Negative Sentiment:
- Add a node that processes negative sentiment responses, such as alerting support teams or initiating a follow-up action.
- For Neutral Sentiment:
- Add a node that processes neutral sentiment responses, such as storing the feedback for future analysis.
- Connect the respective outputs of the Condition node to these nodes.
5. Finalize the Flow:
- Ensure all paths lead to a “Flow output” node, which will return the appropriate response to the user.
- Save and test your flow using sample inputs to verify its behavior.
By following these steps, you’ve created a sentiment analysis application using Amazon Bedrock’s Flow Builder. This visual approach simplifies the development of complex AI workflows, allowing for rapid iteration and deployment.
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