# Sample config for the GAIA GenAI platform module with an existing
# Cognito User Pool. Use this variant when integrating GAIA with a
# pre-existing Cognito User Pool and app client, for example when
# sharing authentication infrastructure across multiple applications.
#
# Differs from the primary config only in the auth section, which
# references an existing Cognito pool instead of creating a new one.

# Complete GAIA generative AI platform configuration including RAG
# engines, LLM models, authentication, VPC networking, and chatbot
# API setup.
gaia:
  # See CONFIGURATION.md for role reference options (name, arn, id).
  # Admin roles granted access to GAIA app resources (like
  # KMS/Bucket). Roles can be referenced by name, arn, or id.
  dataAdminRoles:
    - name: Admin
  # Prefix for GAIA resource naming
  prefix: testing
  # (Optional) Amazon Bedrock integration configuration for
  # foundation model access
  bedrock:
    # Enables or disables Amazon Bedrock foundation model
    # integration
    enabled: true
    # AWS region for Bedrock foundation model access
    # (enum: SupportedRegion values)
    region: 'ca-central-1'
  # LLM model configurations
  llms:
    # SageMaker-hosted LLM model configurations
    sagemaker: []
  # (Optional) RAG engine and embedding model configuration
  rag:
    # Retrieval engine configuration
    engines:
      # (Optional) Aurora RAG engine
      aurora:
        # (Optional) Minimum Aurora Serverless capacity units
        minCapacity: 0.5
        # (Optional) Maximum Aurora Serverless capacity units
        maxCapacity: 4
      # (Optional) Bedrock Knowledge Base RAG engine
      knowledgeBase:
        # (Optional) External Knowledge Base configurations
        external:
          - # Knowledge Base ID
            kbId: test-kb-id
            # Name for the external Knowledge Base
            name: test-external-kb
    # Embedding models for vector processing
    embeddingsModels:
      - # Model provider (enum: bedrock, openai, sagemaker)
        provider: 'bedrock'
        # Model name identifier
        name: 'amazon.titan-embed-text-v2:0'
        # Vector embedding dimensions
        dimensions: 1024
        # (Optional) Whether this is the default model
        isDefault: true
    # Cross-encoder models for document ranking
    crossEncoderModels: []
  # Authentication configuration for user access.
  # This config uses an existing Cognito User Pool.
  auth:
    # Authentication method for the GAIA platform
    # (enum: email_pass, ad, existing)
    authType: 'existing'
    # Existing Cognito User Pool ID for integrating GAIA with a
    # pre-existing user pool. Required when authType is 'existing'.
    existingPoolId: '{{region}}_TestPoolId'
    # (Optional) Existing Cognito User Pool app client ID
    existingPoolClientId: 'test-app-client-id-12345'
    # (Optional) Existing Cognito User Pool domain
    existingPoolDomain: 'existing-pool-domain'
  # VPC networking configuration for GAIA infrastructure
  vpc:
    # VPC identifier for hosting GAIA application components
    # Often created by your VPC/networking stack.
    # Example SSM: ssm:/path/to/vpc/id
    vpcId: XXXXXXXX
    # Security group ID for application tier components
    # Often created by your VPC/networking stack.
    # Example SSM: ssm:/path/to/security-group/id
    appSecurityGroupId: 'sg-12345351356'
    # Subnet IDs for the application tier
    # Often created by your VPC/networking stack.
    # Example SSM: ssm:/path/to/subnet/id
    appSubnets:
      - subnet1
    # Security group ID for data tier components
    # Often created by your VPC/networking stack.
    # Example SSM: ssm:/path/to/security-group/id
    dataSecurityGroupId: 'sg-46576878787'
    # Subnet IDs for the data tier
    # Often created by your VPC/networking stack.
    # Example SSM: ssm:/path/to/subnet/id
    dataSubnets:
      - subnet2