# LiteLLM Proxy Configuration Example for AWS Deployment
#
# This is an example configuration file for LiteLLM proxy server.
# Customize this file with your actual API keys and model configurations.
#
# To use this configuration with your AWS deployment:
# 1. Update the model_list with your actual providers and API keys
# 2. Store API keys in AWS Secrets Manager
# 3. Update the ECS task definition to use this config
#
# Documentation: https://docs.litellm.ai/docs/proxy/configs

model_list:
  # OpenAI Models
  - model_name: gpt-4
    litellm_params:
      model: gpt-4
      api_key: os.environ/OPENAI_API_KEY

  - model_name: gpt-4-turbo
    litellm_params:
      model: gpt-4-turbo-preview
      api_key: os.environ/OPENAI_API_KEY

  - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo
      api_key: os.environ/OPENAI_API_KEY

  # Anthropic Claude Models
  - model_name: claude-3-opus
    litellm_params:
      model: anthropic/claude-3-opus-20240229
      api_key: os.environ/ANTHROPIC_API_KEY

  - model_name: claude-3-sonnet
    litellm_params:
      model: anthropic/claude-3-sonnet-20240229
      api_key: os.environ/ANTHROPIC_API_KEY

  # Fake endpoint for testing (used in benchmarks)
  - model_name: fake-openai-endpoint
    litellm_params:
      model: openai/fake
      api_key: fake-key
      api_base: https://exampleopenaiendpoint-production.up.railway.app/

  # AWS Bedrock Models (using IAM authentication)
  # - model_name: bedrock-claude
  #   litellm_params:
  #     model: bedrock/anthropic.claude-v2
  #     aws_region_name: us-east-1

  # Azure OpenAI Models
  # - model_name: azure-gpt-4
  #   litellm_params:
  #     model: azure/gpt-4
  #     api_key: os.environ/AZURE_OPENAI_API_KEY
  #     api_base: os.environ/AZURE_OPENAI_API_BASE
  #     api_version: "2024-02-01"

  # Google Vertex AI Models
  # - model_name: gemini-pro
  #   litellm_params:
  #     model: vertex_ai/gemini-pro
  #     vertex_project: os.environ/VERTEX_PROJECT
  #     vertex_location: os.environ/VERTEX_LOCATION

litellm_settings:
  # Enable detailed logging
  set_verbose: false

  # Request timeout
  request_timeout: 600

  # Enable fallbacks on errors
  fallbacks: []

  # Context window fallbacks
  context_window_fallbacks: []

  # Enable content moderation (requires provider support)
  # content_policy_fallbacks: []

router_settings:
  # Routing strategy: "simple-shuffle" | "latency-based-routing" | "least-busy" | "usage-based-routing"
  routing_strategy: latency-based-routing

  # Enable retry on failure
  num_retries: 2

  # Timeout for retries
  timeout: 300

  # Redis for caching router decisions (optional but recommended)
  # redis_host: os.environ/REDIS_HOST
  # redis_port: os.environ/REDIS_PORT
  # redis_password: os.environ/REDIS_PASSWORD

  # Model-specific cooldown after errors (seconds)
  allowed_fails: 3
  cooldown_time: 60

general_settings:
  # Master key for API authentication (required)
  master_key: os.environ/PROXY_MASTER_KEY

  # Database URL (automatically set by ECS task definition)
  database_url: os.environ/DATABASE_URL

  # Store model information in database
  store_model_in_db: true

  # Enable batch writing to reduce database load
  # Recommended for high throughput (1-2K RPS)
  proxy_batch_write_at: 60

  # Enable logging to external services
  # success_callback: ["langsmith", "lunary"]
  # failure_callback: ["langsmith", "lunary"]

  # Alert webhooks
  # alerting: ["slack"]
  # alerting_threshold: 300  # seconds

  # Cost tracking
  # max_budget: 100  # USD
  # budget_duration: 30d

# Optional: Cache configuration
# Reduces database load by 60-80%
# cache:
#   type: redis
#   host: os.environ/REDIS_HOST
#   port: os.environ/REDIS_PORT
#   password: os.environ/REDIS_PASSWORD
#   ttl: 600  # Cache TTL in seconds

# Optional: Prometheus metrics
# prometheus:
#   enabled: true
#   port: 9090

# Optional: Admin UI settings
# ui_settings:
#   master_key: os.environ/PROXY_MASTER_KEY
#   disable_ui: false

# Optional: Rate limiting
# rate_limit:
#   rpm: 60  # Requests per minute per key
#   tpm: 1000  # Tokens per minute per key

# Optional: Team/User management
# team_settings:
#   - team_id: team_1
#     max_budget: 50
#     budget_duration: 30d
#     models: ["gpt-4", "gpt-3.5-turbo"]

# Optional: Guardrails (content filtering, PII detection)
# guardrails:
#   - guardrail_name: "pii-detection"
#     litellm_params:
#       guardrail: presidio
#       mode: "during_call"

# Optional: Logging integrations
# langsmith:
#   api_key: os.environ/LANGSMITH_API_KEY
#   project: litellm-proxy

# Optional: Alerting integrations
# slack:
#   webhook_url: os.environ/SLACK_WEBHOOK_URL

# Environment variables to set in ECS task definition:
# - OPENAI_API_KEY: Your OpenAI API key
# - ANTHROPIC_API_KEY: Your Anthropic API key
# - PROXY_MASTER_KEY: Master key for proxy authentication
# - DATABASE_URL: PostgreSQL connection string (auto-set)
# - REDIS_HOST: Redis host (optional)
# - REDIS_PORT: Redis port (optional)
# - REDIS_PASSWORD: Redis password (optional)