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