/aws-cloudformation-bedrock

name: aws-cloudformation-bedrock
description: Provides AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference profiles. Use when creating Bedrock agents with action groups, implementing RAG with knowledge bases, configuring vector stores, setting up content moderation guardrails, managing prompts, orchestrating workflows with flows, and configuring inference profiles for model optimization.
allowed-tools: Read, Write, Bash

AWS CloudFormation Amazon Bedrock

Overview

Creates production-ready AI infrastructure using AWS CloudFormation templates for Amazon Bedrock. Covers Bedrock agents, knowledge bases for RAG implementations, data source connectors, guardrails for content moderation, prompt management, workflow orchestration with flows, and inference profiles for optimized model access.

When to Use

• Creating Bedrock agents with action groups
• Implementing RAG with knowledge bases
• Configuring S3 or web crawl data sources
• Setting up content moderation guardrails
• Managing prompt templates
• Orchestrating AI workflows with Bedrock Flows
• Configuring inference profiles for multi-model access
• Organizing templates with Parameters and cross-stack references

Instructions

1. Define Parameters

Parameters:
  FoundationModel:
    Type: String
    Default: anthropic.claude-3-sonnet-20240229-v1:0
    AllowedValues:
      - anthropic.claude-3-sonnet-20240229-v1:0
      - anthropic.claude-3-haiku-20240307-v1:0
      - amazon.titan-text-express-v1
    Description: Foundation model for agent

2. Create Agent Role

Resources:
  AgentRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Principal:
              Service: bedrock.amazonaws.com
            Action: sts:AssumeRole
      Policies:
        - PolicyName: BedrockPermissions
          PolicyDocument:
            Version: "2012-10-17"
            Statement:
              - Effect: Allow
                Action:
                  - bedrock:InvokeModel
                Resource: !Sub "arn:aws:bedrock:${AWS::Region}:${AWS::AccountId}:foundation-model/${FoundationModel}"

3. Create Agent

  BedrockAgent:
    Type: AWS::Bedrock::Agent
    Properties:
      AgentName: !Sub "${AWS::StackName}-agent"
      AgentResourceRoleArn: !GetAtt AgentRole.Arn
      FoundationModelArn: !Sub "arn:aws:bedrock:${AWS::Region}::foundation-model/${FoundationModel}"
      AutoPrepare: true
      Instruction: |
        You are a helpful assistant. Use the knowledge base to answer questions.

4. Create Knowledge Base

  KnowledgeBaseRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Principal:
              Service: bedrock.amazonaws.com
            Action: sts:AssumeRole

  KnowledgeBase:
    Type: AWS::Bedrock::KnowledgeBase
    Properties:
      Name: !Sub "${AWS::StackName}-kb"
      RoleArn: !GetAtt KnowledgeBaseRole.Arn
      KnowledgeBaseConfiguration:
        Type: VECTOR
        VectorKnowledgeBaseConfiguration:
          EmbeddingModelArn: !Sub "arn:aws:bedrock:${AWS::Region}::embedding-model/amazon.titan-embed-text-v1"

5. Create Data Source

  DataBucket:
    Type: AWS::S3::Bucket

  S3DataSource:
    Type: AWS::Bedrock::DataSource
    Properties:
      KnowledgeBaseId: !Ref KnowledgeBase
      Name: s3-data-source
      Type: S3
      DataSourceConfiguration:
        S3Configuration:
          BucketArn: !GetAtt DataBucket.Arn
          InclusionPrefixes:
            - documents/

6. Add Guardrail

  Guardrail:
    Type: AWS::Bedrock::Guardrail
    Properties:
      Name: !Sub "${AWS::StackName}-guardrail"
      BlockedInputMessaging: "I cannot help with that request."
      ContentPolicyConfig:
        filtersConfig:
          - type: PROFANITY
          - type: MISCONDUCT

7. Create Action Group

  ActionLambdaFunction:
    Type: AWS::Lambda::Function
    Properties:
      Runtime: python3.12
      Handler: index.handler
      Role: !GetAtt ActionLambdaRole.Arn
      Code:
        ZipFile: |
          def handler(event, context):
              return {"statusCode": 200, "body": "{\"result\": \"success\"}"}

  ActionGroup:
    Type: AWS::Bedrock::AgentActionGroup
    Properties:
      ActionGroupName: api-operations
      ActionGroupState: ENABLED
      AgentId: !GetAtt BedrockAgent.AgentId
      ActionGroupExecutor:
        Lambda: !Ref ActionLambdaFunction
      FunctionSchema:
        functionConfigurations:
          - function: |
              { "name": "get_inventory", "description": "Get current inventory status", "parameters": { "type": "object", "properties": { "sku": { "type": "string" } }, "required": [] } }

8. Validate Before Deploy

Always validate the template before deployment:

aws cloudformation validate-template --template-body file://bedrock-template.yaml

9. Verify After Deploy

# Check agent status
aws bedrock-agent get-agent --agent-id $(aws cloudformation describe-stacks --stack-name STACK_NAME --query 'Stacks[0].Outputs[?OutputKey==`AgentId`].OutputValue' --output text)

# Check knowledge base sync status
aws bedrock-agent list-knowledge-bases --agent-id AGENT_ID

# Test guardrail
aws bedrock-runtime apply_guardrail --guardrail-identifier GUARDRAIL_ID --source SOURCE

Examples

Minimal RAG Agent Template

Complete working template for a RAG-enabled agent:

AWSTemplateFormatVersion: "2010-09-09"
Description: "Bedrock RAG Agent with Knowledge Base"

Parameters:
  FoundationModel:
    Type: String
    Default: anthropic.claude-3-sonnet-20240229-v1:0

Resources:
  # IAM Role for Agent
  AgentRole:
    Type: AWS::IAM::Role
    Properties: