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760aa38225
Add full MiniMax provider support across the entire stack: Backend: - Add MINIMAX to LiteLLMProvider enum in db.py - Add MINIMAX mapping to all provider_map dicts in llm_service.py, llm_router_service.py, and llm_config.py - Add Alembic migration (rev 106) for PostgreSQL enum - Add MiniMax M2.5 example in global_llm_config.example.yaml Frontend: - Add MiniMax to LLM_PROVIDERS enum with apiBase - Add MiniMax-M2.5 and MiniMax-M2.5-highspeed to LLM_MODELS - Add MINIMAX to Zod validation schema - Add MiniMax SVG icon and wire up in provider-icons Docs: - Add MiniMax setup guide in chinese-llm-setup.md MiniMax uses an OpenAI-compatible API (https://api.minimax.io/v1) with models supporting up to 204K context window. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
285 lines
10 KiB
YAML
285 lines
10 KiB
YAML
# Global LLM Configuration
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#
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# SETUP INSTRUCTIONS:
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# 1. For production: Copy this file to global_llm_config.yaml and add your real API keys
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# 2. For testing: The system will use this example file automatically if global_llm_config.yaml doesn't exist
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#
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# NOTE: The example API keys below are placeholders and won't work.
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# Replace them with your actual API keys to enable global configurations.
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#
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# These configurations will be available to all users as a convenient option
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# Users can choose to use these global configs or add their own
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#
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# AUTO MODE (Recommended):
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# - Auto mode (ID: 0) uses LiteLLM Router to automatically load balance across all global configs
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# - This helps avoid rate limits by distributing requests across multiple providers
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# - New users are automatically assigned Auto mode by default
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# - Configure router_settings below to customize the load balancing behavior
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#
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# Structure matches NewLLMConfig:
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# - LLM model configuration (provider, model_name, api_key, etc.)
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# - Prompt configuration (system_instructions, citations_enabled)
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# Router Settings for Auto Mode
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# These settings control how the LiteLLM Router distributes requests across models
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router_settings:
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# Routing strategy options:
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# - "usage-based-routing": Routes to deployment with lowest current usage (recommended for rate limits)
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# - "simple-shuffle": Random distribution with optional RPM/TPM weighting
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# - "least-busy": Routes to least busy deployment
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# - "latency-based-routing": Routes based on response latency
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routing_strategy: "usage-based-routing"
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# Number of retries before failing
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num_retries: 3
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# Number of failures allowed before cooling down a deployment
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allowed_fails: 3
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# Cooldown time in seconds after allowed_fails is exceeded
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cooldown_time: 60
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# Fallback models (optional) - when primary fails, try these
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# Format: [{"primary_model": ["fallback1", "fallback2"]}]
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# fallbacks: []
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global_llm_configs:
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# Example: OpenAI GPT-4 Turbo with citations enabled
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- id: -1
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name: "Global GPT-4 Turbo"
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description: "OpenAI's GPT-4 Turbo with default prompts and citations"
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provider: "OPENAI"
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model_name: "gpt-4-turbo-preview"
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api_key: "sk-your-openai-api-key-here"
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api_base: ""
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# Rate limits for load balancing (requests/tokens per minute)
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rpm: 500 # Requests per minute
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tpm: 100000 # Tokens per minute
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litellm_params:
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temperature: 0.7
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max_tokens: 4000
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# Prompt Configuration
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system_instructions: "" # Empty = use default SURFSENSE_SYSTEM_INSTRUCTIONS
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use_default_system_instructions: true
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citations_enabled: true
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# Example: Anthropic Claude 3 Opus
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- id: -2
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name: "Global Claude 3 Opus"
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description: "Anthropic's most capable model with citations"
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provider: "ANTHROPIC"
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model_name: "claude-3-opus-20240229"
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api_key: "sk-ant-your-anthropic-api-key-here"
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api_base: ""
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rpm: 1000
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tpm: 100000
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litellm_params:
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temperature: 0.7
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max_tokens: 4000
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system_instructions: ""
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use_default_system_instructions: true
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citations_enabled: true
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# Example: Fast model - GPT-3.5 Turbo (citations disabled for speed)
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- id: -3
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name: "Global GPT-3.5 Turbo (Fast)"
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description: "Fast responses without citations for quick queries"
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provider: "OPENAI"
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model_name: "gpt-3.5-turbo"
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api_key: "sk-your-openai-api-key-here"
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api_base: ""
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rpm: 3500 # GPT-3.5 has higher rate limits
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tpm: 200000
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litellm_params:
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temperature: 0.5
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max_tokens: 2000
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system_instructions: ""
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use_default_system_instructions: true
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citations_enabled: false # Disabled for faster responses
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# Example: Chinese LLM - DeepSeek with custom instructions
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- id: -4
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name: "Global DeepSeek Chat (Chinese)"
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description: "DeepSeek optimized for Chinese language responses"
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provider: "DEEPSEEK"
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model_name: "deepseek-chat"
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api_key: "your-deepseek-api-key-here"
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api_base: "https://api.deepseek.com/v1"
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rpm: 60
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tpm: 100000
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litellm_params:
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temperature: 0.7
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max_tokens: 4000
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# Custom system instructions for Chinese responses
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system_instructions: |
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<system_instruction>
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You are SurfSense, a reasoning and acting AI agent designed to answer user questions using the user's personal knowledge base.
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Today's date (UTC): {resolved_today}
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IMPORTANT: Please respond in Chinese (简体中文) unless the user specifically requests another language.
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</system_instruction>
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use_default_system_instructions: false
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citations_enabled: true
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# Example: Azure OpenAI GPT-4o
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# IMPORTANT: For Azure deployments, always include 'base_model' in litellm_params
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# to enable accurate token counting, cost tracking, and max token limits
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- id: -5
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name: "Global Azure GPT-4o"
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description: "Azure OpenAI GPT-4o deployment"
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provider: "AZURE"
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# model_name format for Azure: azure/<your-deployment-name>
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model_name: "azure/gpt-4o-deployment"
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api_key: "your-azure-api-key-here"
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api_base: "https://your-resource.openai.azure.com"
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api_version: "2024-02-15-preview" # Azure API version
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rpm: 1000
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tpm: 150000
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litellm_params:
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temperature: 0.7
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max_tokens: 4000
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# REQUIRED for Azure: Specify the underlying OpenAI model
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# This fixes "Could not identify azure model" warnings
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# Common base_model values: gpt-4, gpt-4-turbo, gpt-4o, gpt-4o-mini, gpt-3.5-turbo
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base_model: "gpt-4o"
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system_instructions: ""
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use_default_system_instructions: true
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citations_enabled: true
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# Example: Azure OpenAI GPT-4 Turbo
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- id: -6
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name: "Global Azure GPT-4 Turbo"
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description: "Azure OpenAI GPT-4 Turbo deployment"
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provider: "AZURE"
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model_name: "azure/gpt-4-turbo-deployment"
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api_key: "your-azure-api-key-here"
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api_base: "https://your-resource.openai.azure.com"
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api_version: "2024-02-15-preview"
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rpm: 500
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tpm: 100000
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litellm_params:
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temperature: 0.7
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max_tokens: 4000
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base_model: "gpt-4-turbo" # Maps to gpt-4-turbo-preview
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system_instructions: ""
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use_default_system_instructions: true
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citations_enabled: true
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# Example: Groq - Fast inference
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- id: -7
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name: "Global Groq Llama 3"
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description: "Ultra-fast Llama 3 70B via Groq"
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provider: "GROQ"
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model_name: "llama3-70b-8192"
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api_key: "your-groq-api-key-here"
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api_base: ""
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rpm: 30 # Groq has lower rate limits on free tier
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tpm: 14400
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litellm_params:
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temperature: 0.7
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max_tokens: 8000
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system_instructions: ""
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use_default_system_instructions: true
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citations_enabled: true
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# Example: MiniMax M2.5 - High-performance with 204K context window
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- id: -8
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name: "Global MiniMax M2.5"
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description: "MiniMax M2.5 with 204K context window and competitive pricing"
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provider: "MINIMAX"
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model_name: "MiniMax-M2.5"
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api_key: "your-minimax-api-key-here"
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api_base: "https://api.minimax.io/v1"
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rpm: 60
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tpm: 100000
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litellm_params:
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temperature: 1.0 # MiniMax requires temperature in (0.0, 1.0], cannot be 0
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max_tokens: 4000
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system_instructions: ""
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use_default_system_instructions: true
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citations_enabled: true
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# =============================================================================
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# Image Generation Configuration
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# =============================================================================
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# These configurations power the image generation feature using litellm.aimage_generation().
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# Supported providers: OpenAI, Azure, Google AI Studio, Vertex AI, AWS Bedrock,
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# Recraft, OpenRouter, Xinference, Nscale
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#
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# Auto mode (ID 0) uses LiteLLM Router for load balancing across all image gen configs.
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# Router Settings for Image Generation Auto Mode
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image_generation_router_settings:
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routing_strategy: "usage-based-routing"
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num_retries: 3
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allowed_fails: 3
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cooldown_time: 60
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global_image_generation_configs:
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# Example: OpenAI DALL-E 3
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- id: -1
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name: "Global DALL-E 3"
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description: "OpenAI's DALL-E 3 for high-quality image generation"
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provider: "OPENAI"
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model_name: "dall-e-3"
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api_key: "sk-your-openai-api-key-here"
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api_base: ""
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rpm: 50 # Requests per minute (image gen is rate-limited by RPM, not tokens)
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litellm_params: {}
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# Example: OpenAI GPT Image 1
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- id: -2
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name: "Global GPT Image 1"
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description: "OpenAI's GPT Image 1 model"
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provider: "OPENAI"
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model_name: "gpt-image-1"
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api_key: "sk-your-openai-api-key-here"
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api_base: ""
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rpm: 50
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litellm_params: {}
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# Example: Azure OpenAI DALL-E 3
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- id: -3
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name: "Global Azure DALL-E 3"
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description: "Azure-hosted DALL-E 3 deployment"
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provider: "AZURE_OPENAI"
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model_name: "azure/dall-e-3-deployment"
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api_key: "your-azure-api-key-here"
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api_base: "https://your-resource.openai.azure.com"
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api_version: "2024-02-15-preview"
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rpm: 50
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litellm_params:
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base_model: "dall-e-3"
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# Example: OpenRouter Gemini Image Generation
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# - id: -4
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# name: "Global Gemini Image Gen"
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# description: "Google Gemini image generation via OpenRouter"
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# provider: "OPENROUTER"
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# model_name: "google/gemini-2.5-flash-image"
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# api_key: "your-openrouter-api-key-here"
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# api_base: ""
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# rpm: 30
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# litellm_params: {}
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# Notes:
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# - ID 0 is reserved for "Auto" mode - uses LiteLLM Router for load balancing
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# - Use negative IDs to distinguish global configs from user configs (NewLLMConfig in DB)
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# - IDs should be unique and sequential (e.g., -1, -2, -3, etc.)
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# - The 'api_key' field will not be exposed to users via API
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# - system_instructions: Custom prompt or empty string to use defaults
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# - use_default_system_instructions: true = use SURFSENSE_SYSTEM_INSTRUCTIONS when system_instructions is empty
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# - citations_enabled: true = include citation instructions, false = include anti-citation instructions
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# - All standard LiteLLM providers are supported
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# - rpm/tpm: Optional rate limits for load balancing (requests/tokens per minute)
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# These help the router distribute load evenly and avoid rate limit errors
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#
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#
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# IMAGE GENERATION NOTES:
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# - Image generation configs use the same ID scheme as LLM configs (negative for global)
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# - Supported models: dall-e-2, dall-e-3, gpt-image-1 (OpenAI), azure/* (Azure),
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# bedrock/* (AWS), vertex_ai/* (Google), recraft/* (Recraft), openrouter/* (OpenRouter)
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# - The router uses litellm.aimage_generation() for async image generation
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# - Only RPM (requests per minute) is relevant for image generation rate limiting.
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# TPM (tokens per minute) does not apply since image APIs are billed/rate-limited per request, not per token.
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