fetch_ml/configs/examples/config-postgres.yaml
Jeremie Fraeys 3de1e6e9ab feat: add comprehensive configuration and deployment infrastructure
- Add development and production configuration templates
- Include Docker build files for containerized deployment
- Add Nginx configuration with SSL/TLS setup
- Include environment configuration examples
- Add SSL certificate setup and management
- Configure application schemas and validation
- Support for both local and production deployment scenarios

Provides flexible deployment options from development to production
with proper security, monitoring, and configuration management.
2025-12-04 16:54:02 -05:00

64 lines
1.3 KiB
YAML

# Fetch ML Configuration Example for PostgreSQL
# This example shows how to configure Fetch ML to use PostgreSQL as the database
auth:
enabled: true
apikeys:
admin:
hash: "5e884898da28047151d0e56f8dc6292773603d0d6aabbdd5f8b6c8b0b4f0b8e3" # "password"
admin: true
roles: ["admin"]
server:
address: ":9101"
tls:
enabled: false
database:
type: "postgres"
host: "localhost"
port: 5432
username: "fetchml"
password: "your_password_here"
database: "fetchml"
# Alternatively, you can use a full connection string:
# connection: "postgres://fetchml:your_password_here@localhost:5432/fetchml?sslmode=disable"
redis:
host: "localhost"
port: 6379
password: ""
db: 0
pool_size: 10
max_retries: 3
logging:
level: "info"
console: true
format: "text"
security:
secret_key: "your-secret-key-here-at-least-16-characters"
jwt_expiry: "24h"
rate_limit:
enabled: false
requests_per_minute: 60
burst_size: 10
containers:
runtime: "podman"
registry: "docker.io"
pull_policy: "missing"
resources:
cpu_limit: "2"
memory_limit: "4Gi"
gpu_limit: 1
storage:
data_path: "data"
results_path: "results"
temp_path: "/tmp/fetch_ml"
cleanup:
enabled: true
max_age_hours: 168
max_size_gb: 10