158 lines
5.3 KiB
Python
158 lines
5.3 KiB
Python
"""
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Global configuration for Core Code API
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"""
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from pydantic_settings import BaseSettings
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from functools import lru_cache
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# Import infrastructure credentials from gitignored module
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try:
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from src.credentials import (
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PORTAINER_URL, PORTAINER_API_KEY,
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NPM_URL, NPM_EMAIL, NPM_PASSWORD,
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KUMA_URL, KUMA_USERNAME, KUMA_PASSWORD, KUMA_API_KEY,
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BRAVE_SEARCH_API_KEY,
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GOOGLE_SEARCH_API_KEY, GOOGLE_SEARCH_ENGINE_ID
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)
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except ImportError:
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# Fallback to empty strings if credentials.py doesn't exist
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# (e.g., fresh clone before credentials setup)
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PORTAINER_URL = "http://localhost:8001"
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PORTAINER_API_KEY = ""
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NPM_URL = "http://localhost:81"
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NPM_EMAIL = ""
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NPM_PASSWORD = ""
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KUMA_URL = "http://localhost:3001"
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KUMA_USERNAME = ""
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KUMA_PASSWORD = ""
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KUMA_API_KEY = ""
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BRAVE_SEARCH_API_KEY = ""
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GOOGLE_SEARCH_API_KEY = ""
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GOOGLE_SEARCH_ENGINE_ID = ""
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class Settings(BaseSettings):
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"""Global application settings"""
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# Application
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app_name: str = "Core Code API"
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app_version: str = "1.0.0"
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debug: bool = False
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# Server
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host: str = "0.0.0.0"
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port: int = 8083
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# CORS
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cors_origins: list[str] = ["*"]
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cors_credentials: bool = True
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cors_methods: list[str] = ["*"]
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cors_headers: list[str] = ["*"]
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# Logging
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log_level: str = "DEBUG"
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# Ollama Configuration (for AI orchestration)
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ollama_base_url: str = "http://ollama:11434"
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ollama_timeout: int = 300 # 5 minutes
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# Model Configuration
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default_model: str = "mistral-tools:7b"
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agent_model: str = "gemma2:9b-instruct-q5_K_M" # Must support tool calling with ADK (~4GB VRAM)
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lightweight_models: str = "gemma3-tools:1b,phi3:mini"
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heavy_models: str = "mistral:7b,gemma2:9b,gemma3:12b,mixtral:8x7b"
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code_models: str = "codestral:latest,codegemma:latest"
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# Previous config (gemma3:12b used ~10GB VRAM)
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# default_model: str = "gemma3:12b"
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# agent_model: str = "gemma3:12b"
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# System Prompt Variant (for A/B testing)
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# Options: v1_verbose, v2_concise, v3_imperative, v4_minimal, v4_gemini_suggestion, v5_adk_optimized, v7_adk_best_practice, v8_holistic
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system_prompt_variant: str = "v8_holistic"
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# Agent Configuration
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agent_fallback_enabled: bool = True
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# Model Aliases (OpenAI → Local)
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alias_gpt35: str = "gemma:7b"
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alias_gpt4: str = "mistral:7b"
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alias_gpt4_turbo: str = "mixtral:8x7b"
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alias_gpt4_code: str = "codestral:latest"
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# Memory Configuration
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memory_tier1_max_turns: int = 10
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memory_consolidation_threshold: int = 10
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# Qdrant Configuration
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qdrant_host: str = "qdrant"
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qdrant_port: int = 6333
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qdrant_collection_conversations: str = "core_api_conversations"
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qdrant_collection_documents: str = "core_api_documents"
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qdrant_collection_user_facts: str = "core_api_user_facts"
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# Embeddings (using Ollama - no local models needed)
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embedding_model: str = "nomic-embed-text" # Ollama embedding model
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embedding_dimension: int = 768 # nomic-embed-text dimension
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embedding_batch_size: int = 32
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# Search Configuration
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search_provider: str = "google" # Options: google, brave, searxng, duckduckgo
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searxng_url: str = "http://searxng:8080" # For future self-hosted SearxNG
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# Search API Keys (from credentials.py)
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brave_search_api_key: str = BRAVE_SEARCH_API_KEY # https://brave.com/search/api/
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google_search_api_key: str = GOOGLE_SEARCH_API_KEY # https://console.cloud.google.com/
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google_search_engine_id: str = GOOGLE_SEARCH_ENGINE_ID # Custom Search Engine ID
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# Infrastructure Management (from credentials.py)
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portainer_url: str = PORTAINER_URL
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portainer_api_key: str = PORTAINER_API_KEY
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npm_url: str = NPM_URL
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npm_email: str = NPM_EMAIL
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npm_password: str = NPM_PASSWORD
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kuma_url: str = KUMA_URL
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kuma_username: str = KUMA_USERNAME
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kuma_password: str = KUMA_PASSWORD
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kuma_api_key: str = KUMA_API_KEY
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# Core-AI Service (AI performance metrics)
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core_ai_base_url: str = "http://core-ai:8086"
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# OIDC Authentication (Authentik)
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oidc_enabled: bool = False # Set to True to require authentication
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oidc_issuer: str = "https://auth.schweitz.net/application/o/core-api/"
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oidc_audience: str = "core-api"
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@property
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def model_aliases(self) -> dict:
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"""Computed property for model aliases"""
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return {
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"gpt-3.5-turbo": self.alias_gpt35,
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"gpt-4": self.alias_gpt4,
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"gpt-4-turbo": self.alias_gpt4_turbo,
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"gpt-4-code": self.alias_gpt4_code,
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}
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def get_lightweight_models(self) -> list[str]:
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"""Parse comma-separated lightweight models"""
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return [m.strip().strip('"').strip("'") for m in self.lightweight_models.split(",") if m.strip()]
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def get_heavy_models(self) -> list[str]:
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"""Parse comma-separated heavy models"""
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return [m.strip().strip('"').strip("'") for m in self.heavy_models.split(",") if m.strip()]
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def get_code_models(self) -> list[str]:
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"""Parse comma-separated code models"""
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return [m.strip().strip('"').strip("'") for m in self.code_models.split(",") if m.strip()]
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class Config:
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env_file = ".env"
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case_sensitive = False
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@lru_cache()
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def get_settings() -> Settings:
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"""Cached settings instance"""
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return Settings()
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