# Global LLM Configuration
# 
# SETUP INSTRUCTIONS:
# 1. For production: Copy this file to global_llm_config.yaml and add your real API keys
# 2. For testing: The system will use this example file automatically if global_llm_config.yaml doesn't exist
#
# NOTE: The example API keys below are placeholders and won't work.
# Replace them with your actual API keys to enable global configurations.
#
# These configurations will be available to all users as a convenient option
# Users can choose to use these global configs or add their own
#
# AUTO MODE (Recommended):
# - Auto mode (ID: 0) uses LiteLLM Router to automatically load balance across all global configs
# - This helps avoid rate limits by distributing requests across multiple providers
# - New users are automatically assigned Auto mode by default
# - Configure router_settings below to customize the load balancing behavior
#
# Structure matches NewLLMConfig:
# - LLM model configuration (provider, model_name, api_key, etc.)
# - Prompt configuration (system_instructions, citations_enabled)

# Router Settings for Auto Mode
# These settings control how the LiteLLM Router distributes requests across models
router_settings:
  # Routing strategy options:
  # - "usage-based-routing": Routes to deployment with lowest current usage (recommended for rate limits)
  # - "simple-shuffle": Random distribution with optional RPM/TPM weighting
  # - "least-busy": Routes to least busy deployment
  # - "latency-based-routing": Routes based on response latency
  routing_strategy: "usage-based-routing"
  
  # Number of retries before failing
  num_retries: 3
  
  # Number of failures allowed before cooling down a deployment
  allowed_fails: 3
  
  # Cooldown time in seconds after allowed_fails is exceeded
  cooldown_time: 60
  
  # Fallback models (optional) - when primary fails, try these
  # Format: [{"primary_model": ["fallback1", "fallback2"]}]
  # fallbacks: []

global_llm_configs:
  # Example: OpenAI GPT-4 Turbo with citations enabled
  - id: -1
    name: "Global GPT-4 Turbo"
    description: "OpenAI's GPT-4 Turbo with default prompts and citations"
    provider: "OPENAI"
    model_name: "gpt-4-turbo-preview"
    api_key: "sk-your-openai-api-key-here"
    api_base: ""
    # Rate limits for load balancing (requests/tokens per minute)
    rpm: 500  # Requests per minute
    tpm: 100000  # Tokens per minute
    litellm_params:
      temperature: 0.7
      max_tokens: 4000
    # Prompt Configuration
    system_instructions: ""  # Empty = use default SURFSENSE_SYSTEM_INSTRUCTIONS
    use_default_system_instructions: true
    citations_enabled: true

  # Example: Anthropic Claude 3 Opus
  - id: -2
    name: "Global Claude 3 Opus"
    description: "Anthropic's most capable model with citations"
    provider: "ANTHROPIC"
    model_name: "claude-3-opus-20240229"
    api_key: "sk-ant-your-anthropic-api-key-here"
    api_base: ""
    rpm: 1000
    tpm: 100000
    litellm_params:
      temperature: 0.7
      max_tokens: 4000
    system_instructions: ""
    use_default_system_instructions: true
    citations_enabled: true

  # Example: Fast model - GPT-3.5 Turbo (citations disabled for speed)
  - id: -3
    name: "Global GPT-3.5 Turbo (Fast)"
    description: "Fast responses without citations for quick queries"
    provider: "OPENAI"
    model_name: "gpt-3.5-turbo"
    api_key: "sk-your-openai-api-key-here"
    api_base: ""
    rpm: 3500  # GPT-3.5 has higher rate limits
    tpm: 200000
    litellm_params:
      temperature: 0.5
      max_tokens: 2000
    system_instructions: ""
    use_default_system_instructions: true
    citations_enabled: false  # Disabled for faster responses

  # Example: Chinese LLM - DeepSeek with custom instructions
  - id: -4
    name: "Global DeepSeek Chat (Chinese)"
    description: "DeepSeek optimized for Chinese language responses"
    provider: "DEEPSEEK"
    model_name: "deepseek-chat"
    api_key: "your-deepseek-api-key-here"
    api_base: "https://api.deepseek.com/v1"
    rpm: 60
    tpm: 100000
    litellm_params:
      temperature: 0.7
      max_tokens: 4000
    # Custom system instructions for Chinese responses
    system_instructions: |
      <system_instruction>
      You are SurfSense, a reasoning and acting AI agent designed to answer user questions using the user's personal knowledge base.
      
      Today's date (UTC): {resolved_today}
      
      IMPORTANT: Please respond in Chinese (简体中文) unless the user specifically requests another language.
      </system_instruction>
    use_default_system_instructions: false
    citations_enabled: true

  # Example: Azure OpenAI GPT-4o
  # IMPORTANT: For Azure deployments, always include 'base_model' in litellm_params
  # to enable accurate token counting, cost tracking, and max token limits
  - id: -5
    name: "Global Azure GPT-4o"
    description: "Azure OpenAI GPT-4o deployment"
    provider: "AZURE"
    # model_name format for Azure: azure/<your-deployment-name>
    model_name: "azure/gpt-4o-deployment"
    api_key: "your-azure-api-key-here"
    api_base: "https://your-resource.openai.azure.com"
    api_version: "2024-02-15-preview"  # Azure API version
    rpm: 1000
    tpm: 150000
    litellm_params:
      temperature: 0.7
      max_tokens: 4000
      # REQUIRED for Azure: Specify the underlying OpenAI model
      # This fixes "Could not identify azure model" warnings
      # Common base_model values: gpt-4, gpt-4-turbo, gpt-4o, gpt-4o-mini, gpt-3.5-turbo
      base_model: "gpt-4o"
    system_instructions: ""
    use_default_system_instructions: true
    citations_enabled: true

  # Example: Azure OpenAI GPT-4 Turbo
  - id: -6
    name: "Global Azure GPT-4 Turbo"
    description: "Azure OpenAI GPT-4 Turbo deployment"
    provider: "AZURE"
    model_name: "azure/gpt-4-turbo-deployment"
    api_key: "your-azure-api-key-here"
    api_base: "https://your-resource.openai.azure.com"
    api_version: "2024-02-15-preview"
    rpm: 500
    tpm: 100000
    litellm_params:
      temperature: 0.7
      max_tokens: 4000
      base_model: "gpt-4-turbo"  # Maps to gpt-4-turbo-preview
    system_instructions: ""
    use_default_system_instructions: true
    citations_enabled: true

  # Example: Groq - Fast inference
  - id: -7
    name: "Global Groq Llama 3"
    description: "Ultra-fast Llama 3 70B via Groq"
    provider: "GROQ"
    model_name: "llama3-70b-8192"
    api_key: "your-groq-api-key-here"
    api_base: ""
    rpm: 30  # Groq has lower rate limits on free tier
    tpm: 14400
    litellm_params:
      temperature: 0.7
      max_tokens: 8000
    system_instructions: ""
    use_default_system_instructions: true
    citations_enabled: true

  # Example: MiniMax M2.5 - High-performance with 204K context window
  - id: -8
    name: "Global MiniMax M2.5"
    description: "MiniMax M2.5 with 204K context window and competitive pricing"
    provider: "MINIMAX"
    model_name: "MiniMax-M2.5"
    api_key: "your-minimax-api-key-here"
    api_base: "https://api.minimax.io/v1"
    rpm: 60
    tpm: 100000
    litellm_params:
      temperature: 1.0  # MiniMax requires temperature in (0.0, 1.0], cannot be 0
      max_tokens: 4000
    system_instructions: ""
    use_default_system_instructions: true
    citations_enabled: true

# =============================================================================
# Image Generation Configuration
# =============================================================================
# These configurations power the image generation feature using litellm.aimage_generation().
# Supported providers: OpenAI, Azure, Google AI Studio, Vertex AI, AWS Bedrock,
# Recraft, OpenRouter, Xinference, Nscale
#
# Auto mode (ID 0) uses LiteLLM Router for load balancing across all image gen configs.

# Router Settings for Image Generation Auto Mode
image_generation_router_settings:
  routing_strategy: "usage-based-routing"
  num_retries: 3
  allowed_fails: 3
  cooldown_time: 60

global_image_generation_configs:
  # Example: OpenAI DALL-E 3
  - id: -1
    name: "Global DALL-E 3"
    description: "OpenAI's DALL-E 3 for high-quality image generation"
    provider: "OPENAI"
    model_name: "dall-e-3"
    api_key: "sk-your-openai-api-key-here"
    api_base: ""
    rpm: 50  # Requests per minute (image gen is rate-limited by RPM, not tokens)
    litellm_params: {}

  # Example: OpenAI GPT Image 1
  - id: -2
    name: "Global GPT Image 1"
    description: "OpenAI's GPT Image 1 model"
    provider: "OPENAI"
    model_name: "gpt-image-1"
    api_key: "sk-your-openai-api-key-here"
    api_base: ""
    rpm: 50
    litellm_params: {}

  # Example: Azure OpenAI DALL-E 3
  - id: -3
    name: "Global Azure DALL-E 3"
    description: "Azure-hosted DALL-E 3 deployment"
    provider: "AZURE_OPENAI"
    model_name: "azure/dall-e-3-deployment"
    api_key: "your-azure-api-key-here"
    api_base: "https://your-resource.openai.azure.com"
    api_version: "2024-02-15-preview"
    rpm: 50
    litellm_params:
      base_model: "dall-e-3"

  # Example: OpenRouter Gemini Image Generation
  # - id: -4
  #   name: "Global Gemini Image Gen"
  #   description: "Google Gemini image generation via OpenRouter"
  #   provider: "OPENROUTER"
  #   model_name: "google/gemini-2.5-flash-image"
  #   api_key: "your-openrouter-api-key-here"
  #   api_base: ""
  #   rpm: 30
  #   litellm_params: {}

# Notes:
# - ID 0 is reserved for "Auto" mode - uses LiteLLM Router for load balancing
# - Use negative IDs to distinguish global configs from user configs (NewLLMConfig in DB)
# - IDs should be unique and sequential (e.g., -1, -2, -3, etc.)
# - The 'api_key' field will not be exposed to users via API
# - system_instructions: Custom prompt or empty string to use defaults
# - use_default_system_instructions: true = use SURFSENSE_SYSTEM_INSTRUCTIONS when system_instructions is empty
# - citations_enabled: true = include citation instructions, false = include anti-citation instructions
# - All standard LiteLLM providers are supported
# - rpm/tpm: Optional rate limits for load balancing (requests/tokens per minute)
#   These help the router distribute load evenly and avoid rate limit errors
#
#
# IMAGE GENERATION NOTES:
# - Image generation configs use the same ID scheme as LLM configs (negative for global)
# - Supported models: dall-e-2, dall-e-3, gpt-image-1 (OpenAI), azure/* (Azure), 
#   bedrock/* (AWS), vertex_ai/* (Google), recraft/* (Recraft), openrouter/* (OpenRouter)
# - The router uses litellm.aimage_generation() for async image generation
# - Only RPM (requests per minute) is relevant for image generation rate limiting.
#   TPM (tokens per minute) does not apply since image APIs are billed/rate-limited per request, not per token.