# 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:
# - Model configuration (provider, model_name, api_key, etc.)
# - Prompt configuration (system_instructions, citations_enabled)
#
# COST-BASED PREMIUM CREDITS:
# Each premium config bills the user's USD-credit balance based on the
# actual provider cost reported by LiteLLM. For models LiteLLM already
# knows (most OpenAI/Anthropic/etc. names) you don't need to do anything.
# For custom Azure deployment names (e.g. an in-house "gpt-5.4" deployment)
# or any model LiteLLM doesn't have in its built-in pricing table, declare
# per-token costs inline so they bill correctly:
#
#   litellm_params:
#     base_model: "my-custom-azure-deploy"
#     # USD per token; e.g. 0.000003 == $3.00 per million input tokens
#     input_cost_per_token: 0.000003
#     output_cost_per_token: 0.000015
#
# OpenRouter dynamic models pull pricing automatically from OpenRouter's
# API — no inline declaration needed. Models without resolvable pricing
# debit $0 from the user's balance and log a WARNING.

# 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"
    billing_tier: "free"
    anonymous_enabled: true
    seo_enabled: true
    seo_slug: "gpt-4-turbo"
    quota_reserve_tokens: 4000
    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"
    billing_tier: "free"
    anonymous_enabled: true
    seo_enabled: true
    seo_slug: "claude-3-opus"
    quota_reserve_tokens: 4000
    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"
    billing_tier: "free"
    anonymous_enabled: true
    seo_enabled: true
    seo_slug: "gpt-3.5-turbo-fast"
    quota_reserve_tokens: 2000
    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"
    billing_tier: "free"
    anonymous_enabled: true
    seo_enabled: true
    seo_slug: "deepseek-chat-chinese"
    quota_reserve_tokens: 4000
    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"
    billing_tier: "free"
    anonymous_enabled: true
    seo_enabled: true
    seo_slug: "azure-gpt-4o"
    quota_reserve_tokens: 4000
    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"
    billing_tier: "free"
    anonymous_enabled: true
    seo_enabled: true
    seo_slug: "azure-gpt-4-turbo"
    quota_reserve_tokens: 4000
    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"
    billing_tier: "free"
    anonymous_enabled: true
    seo_enabled: true
    seo_slug: "groq-llama-3"
    quota_reserve_tokens: 8000
    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"
    billing_tier: "free"
    anonymous_enabled: true
    seo_enabled: true
    seo_slug: "minimax-m2.5"
    quota_reserve_tokens: 4000
    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

# =============================================================================
# OpenRouter Integration
# =============================================================================
# When enabled, dynamically fetches ALL available models from the OpenRouter API
# and injects them as global configs. This gives premium users access to any model
# on OpenRouter (Claude, Gemini, Llama, Mistral, etc.) via their premium token quota,
# while free-tier OpenRouter models show up with a green Free badge and do NOT
# consume premium quota.
# Models are fetched at startup and refreshed periodically in the background.
# All calls go through LiteLLM with the openrouter/ prefix.
openrouter_integration:
  enabled: false
  api_key: "sk-or-your-openrouter-api-key"

  # Tier is derived PER MODEL from OpenRouter's own API signals:
  #   - id ends with ":free"                         -> billing_tier=free
  #   - pricing.prompt AND pricing.completion == "0" -> billing_tier=free
  #   - otherwise                                    -> billing_tier=premium
  # No global billing_tier knob is honored; any legacy value emits a startup warning.

  # Anonymous access is split by tier so operators can expose only free
  # models to no-login users without leaking paid inference.
  anonymous_enabled_paid: false
  anonymous_enabled_free: false

  seo_enabled: false
  # quota_reserve_tokens: tokens reserved per call for quota enforcement
  quota_reserve_tokens: 4000
  # id_offset: base negative ID for dynamically generated configs.
  # Model IDs are derived deterministically via BLAKE2b so they survive
  # catalogue churn. Must not overlap with your static global_llm_configs IDs.
  id_offset: -10000
  # refresh_interval_hours: how often to re-fetch models from OpenRouter (0 = startup only)
  refresh_interval_hours: 24

  # Rate limits for PAID OpenRouter models. These are used by LiteLLM Router
  # for per-deployment accounting when OR premium models participate in the
  # shared sub-agent "auto" pool. They do NOT cap OpenRouter itself — your
  # real account limits live at https://openrouter.ai/settings/limits.
  rpm: 200
  tpm: 1000000

  # Rate limits for FREE OpenRouter models. Informational only: free OR
  # models are intentionally kept OUT of the LiteLLM Router pool, because
  # OpenRouter enforces free-tier limits globally per account (~20 RPM +
  # 50-1000 daily requests across every ":free" model combined) —
  # per-deployment router accounting can't represent a shared bucket
  # correctly. Free OR models stay fully available in the model selector
  # and for user-facing Auto thread pinning.
  free_rpm: 20
  free_tpm: 100000

  # Image generation + vision LLM emission are OPT-IN. OpenRouter's catalogue
  # contains hundreds of image- and vision-capable models; turning these on
  # injects them into the global Image-Generation / Vision-LLM model
  # selectors alongside any static configs. Tier (free/premium) is derived
  # per model the same way it is for chat (`:free` suffix or zero pricing).
  # When a user picks a premium image/vision model the call debits the
  # shared $5 USD-cost-based premium credit pool — so leaving these off
  # avoids surprise quota burn on existing deployments. Default: false.
  image_generation_enabled: false
  vision_enabled: false

  litellm_params:
    max_tokens: 16384
  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: {}

# =============================================================================
# Vision LLM Configuration
# =============================================================================
# These configurations power the vision autocomplete feature (screenshot analysis).
# Only vision-capable models should be used here (e.g. GPT-4o, Gemini Pro, Claude 3).
# Supported providers: OpenAI, Anthropic, Google, Azure OpenAI, Vertex AI, Bedrock,
# xAI, OpenRouter, Ollama, Groq, Together AI, Fireworks AI, DeepSeek, Mistral, Custom
#
# Auto mode (ID 0) uses LiteLLM Router for load balancing across all vision configs.

# Router Settings for Vision LLM Auto Mode
vision_llm_router_settings:
  routing_strategy: "usage-based-routing"
  num_retries: 3
  allowed_fails: 3
  cooldown_time: 60

global_vision_llm_configs:
  # Example: OpenAI GPT-4o (recommended for vision)
  - id: -1
    name: "Global GPT-4o Vision"
    description: "OpenAI's GPT-4o with strong vision capabilities"
    provider: "OPENAI"
    model_name: "gpt-4o"
    api_key: "sk-your-openai-api-key-here"
    api_base: ""
    rpm: 500
    tpm: 100000
    litellm_params:
      temperature: 0.3
      max_tokens: 1000

  # Example: Google Gemini 2.0 Flash
  - id: -2
    name: "Global Gemini 2.0 Flash"
    description: "Google's fast vision model with large context"
    provider: "GOOGLE"
    model_name: "gemini-2.0-flash"
    api_key: "your-google-ai-api-key-here"
    api_base: ""
    rpm: 1000
    tpm: 200000
    litellm_params:
      temperature: 0.3
      max_tokens: 1000

  # Example: Anthropic Claude 3.5 Sonnet
  - id: -3
    name: "Global Claude 3.5 Sonnet Vision"
    description: "Anthropic's Claude 3.5 Sonnet with vision support"
    provider: "ANTHROPIC"
    model_name: "claude-3-5-sonnet-20241022"
    api_key: "sk-ant-your-anthropic-api-key-here"
    api_base: ""
    rpm: 1000
    tpm: 100000
    litellm_params:
      temperature: 0.3
      max_tokens: 1000

  # Example: Azure OpenAI GPT-4o
  # - id: -4
  #   name: "Global Azure GPT-4o Vision"
  #   description: "Azure-hosted GPT-4o for vision analysis"
  #   provider: "AZURE_OPENAI"
  #   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"
  #   rpm: 500
  #   tpm: 100000
  #   litellm_params:
  #     temperature: 0.3
  #     max_tokens: 1000
  #     base_model: "gpt-4o"

# 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.
#
# VISION LLM NOTES:
# - Vision configs use the same ID scheme (negative for global, positive for user DB)
# - Only use vision-capable models (GPT-4o, Gemini, Claude 3, etc.)
# - Lower temperature (0.3) is recommended for accurate screenshot analysis
# - Lower max_tokens (1000) is sufficient since autocomplete produces short suggestions
#
# TOKEN QUOTA & ANONYMOUS ACCESS NOTES:
# - billing_tier: "free" or "premium". Controls whether registered users need premium token quota.
# - anonymous_enabled: true/false. Whether the model appears in the public no-login catalog.
# - seo_enabled: true/false. Whether a /free/<seo_slug> landing page is generated.
# - seo_slug: Stable URL slug for SEO pages. Must be unique. Do NOT change once public.
# - seo_title: Optional HTML title tag override for the model's /free/<slug> page.
# - seo_description: Optional meta description override for the model's /free/<slug> page.
# - quota_reserve_tokens: Tokens reserved before each LLM call for quota enforcement.
#   Independent of litellm_params.max_tokens. Used by the token quota service.