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devops-infra-helm-charts-gcp/helm-templates/bifrost-v1.5.12-latest/values-examples/sqlite-redis.yaml
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2026-08-26 03:39:42 +05:30

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# Configuration: SQLite for config/logs + Redis for vector store
# Usage: helm install bifrost ./bifrost -f values-examples/sqlite-redis.yaml
#
# SECURITY NOTE: This example contains placeholder values that MUST be replaced
# before deployment. Specifically:
# - Redis password must be set to a strong, randomly generated value
# - Provider API keys must be replaced with real keys
# See inline comments for specific requirements.
# Storage configuration
storage:
mode: sqlite
persistence:
enabled: true
size: 10Gi
configStore:
enabled: true
logsStore:
enabled: true
# No PostgreSQL
postgresql:
enabled: false
# Deploy Redis for vector store
vectorStore:
enabled: true
type: redis
redis:
enabled: true
auth:
enabled: true
# REQUIRED: Replace with a strong, randomly generated password
# Example: Use `openssl rand -base64 32` to generate a secure password
# Or set via Helm: --set vectorStore.redis.auth.password="$(openssl rand -base64 32)"
# Or use a Kubernetes secret: --set vectorStore.redis.auth.existingSecret=redis-secret
password: "REPLACE_ME_WITH_STRONG_PASSWORD"
master:
persistence:
enabled: true
size: 8Gi
resources:
limits:
cpu: 500m
memory: 512Mi
requests:
cpu: 250m
memory: 256Mi
# Bifrost configuration
bifrost:
client:
enableLogging: true
providers: {}
# Add your provider keys here
# Enable semantic cache plugin to use Redis vector store
plugins:
semanticCache:
enabled: true
# OPTION 1 (Recommended): Reference to external Kubernetes Secret for OpenAI API key
# Create the secret with: kubectl create secret generic bifrost-semantic-cache --from-literal=openai-key=sk-YOUR_OPENAI_KEY
secretRef:
name: "bifrost-semantic-cache"
key: "openai-key"
# OPTION 2 (Not recommended): Or uncomment to provide keys directly (not secure)
# Remove secretRef above and uncomment the keys below:
config:
provider: "openai"
# keys:
# - "REPLACE_WITH_OPENAI_API_KEY" # Not recommended: use secretRef instead
embedding_model: "text-embedding-3-small"
dimension: 1536
threshold: 0.8
ttl: "5m"