"""
FILE: app/config.py
DESCRIPTION: Zentrale Pydantic-Konfiguration. 
             WP-20: Hybrid-Cloud Modus Support (OpenRouter/Gemini/Ollama).
             FIX: Update auf Gemini 2.5 Serie & Optimierung für Gemma 2 Durchsatz.
VERSION: 0.6.6
STATUS: Active
DEPENDENCIES: os, functools, pathlib, python-dotenv
"""
from __future__ import annotations
import os
from functools import lru_cache
from pathlib import Path
from dotenv import load_dotenv

# WP-20: Lade Umgebungsvariablen aus der .env Datei
# override=True garantiert, dass Änderungen in der .env immer Vorrang haben.
load_dotenv(override=True)

class Settings:
    # --- Qdrant Datenbank ---
    QDRANT_URL: str = os.getenv("QDRANT_URL", "http://127.0.0.1:6333")
    QDRANT_API_KEY: str | None = os.getenv("QDRANT_API_KEY")
    COLLECTION_PREFIX: str = os.getenv("MINDNET_PREFIX", "mindnet_dev")
    
    # WP-22: Vektor-Dimension für das Embedding-Modell (nomic)
    VECTOR_SIZE: int = int(os.getenv("VECTOR_DIM", "768"))
    DISTANCE: str = os.getenv("MINDNET_DISTANCE", "Cosine")
    
    # --- Lokale Embeddings ---
    EMBEDDING_MODEL: str = os.getenv("MINDNET_EMBEDDING_MODEL", "nomic-embed-text")
    MODEL_NAME: str = os.getenv("MINDNET_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
    
    # --- WP-20 Hybrid LLM Provider ---
    # "openrouter" ist primär für den Ingest-Turbo mit Gemma 2 empfohlen.
    MINDNET_LLM_PROVIDER: str = os.getenv("MINDNET_LLM_PROVIDER", "openrouter").lower()
    
    # Google AI Studio (Fallback auf 2.5-Serie)
    GOOGLE_API_KEY: str | None = os.getenv("GOOGLE_API_KEY")
    # "gemini-2.5-flash-lite" ist die skalierbare 2025-Alternative für hohe Last.
    GEMINI_MODEL: str = os.getenv("MINDNET_GEMINI_MODEL", "gemini-2.5-flash-lite")
    
    # OpenRouter Integration (openai/gpt-oss-20b:free oder gemma-2)
    OPENROUTER_API_KEY: str | None = os.getenv("OPENROUTER_API_KEY")
    # "google/gemma-2-9b-it:free" bietet hohe Kapazität bei Kostenfreiheit.
    OPENROUTER_MODEL: str = os.getenv("OPENROUTER_MODEL", "google/gemma-2-9b-it:free")
    
    LLM_FALLBACK_ENABLED: bool = os.getenv("MINDNET_LLM_FALLBACK", "true").lower() == "true"
    
    # --- WP-05 Lokales LLM (Ollama) ---
    OLLAMA_URL: str = os.getenv("MINDNET_OLLAMA_URL", "http://127.0.0.1:11434")
    LLM_MODEL: str = os.getenv("MINDNET_LLM_MODEL", "phi3:mini") 
    PROMPTS_PATH: str = os.getenv("MINDNET_PROMPTS_PATH", "config/prompts.yaml")
    
    # --- Performance & Last-Steuerung ---
    LLM_TIMEOUT: float = float(os.getenv("MINDNET_LLM_TIMEOUT", "300.0"))
    DECISION_CONFIG_PATH: str = os.getenv("MINDNET_DECISION_CONFIG", "config/decision_engine.yaml")
    BACKGROUND_LIMIT: int = int(os.getenv("MINDNET_LLM_BACKGROUND_LIMIT", "2"))

    # --- System-Pfade & Ingestion-Logik ---
    DEBUG: bool = os.getenv("DEBUG", "false").lower() == "true"
    MINDNET_VAULT_ROOT: str = os.getenv("MINDNET_VAULT_ROOT", "./vault_master")
    MINDNET_TYPES_FILE: str = os.getenv("MINDNET_TYPES_FILE", "config/types.yaml")
    MINDNET_VOCAB_PATH: str = os.getenv("MINDNET_VOCAB_PATH", "/mindnet/vault/mindnet/_system/dictionary/edge_vocabulary.md")
    
    # WP-22: 'full' für Multi-Hash Change Detection
    CHANGE_DETECTION_MODE: str = os.getenv("MINDNET_CHANGE_DETECTION_MODE", "full")
    
    # --- WP-04 Retriever Gewichte ---
    RETRIEVER_W_SEM: float = float(os.getenv("MINDNET_WP04_W_SEM", "0.70"))
    RETRIEVER_W_EDGE: float = float(os.getenv("MINDNET_WP04_W_EDGE", "0.25"))
    RETRIEVER_W_CENT: float = float(os.getenv("MINDNET_WP04_W_CENT", "0.05"))
    RETRIEVER_TOP_K: int = int(os.getenv("MINDNET_WP04_TOP_K", "10"))
    RETRIEVER_EXPAND_DEPTH: int = int(os.getenv("MINDNET_WP04_EXPAND_DEPTH", "1"))
    RETRIEVER_EDGE_TYPES: str = os.getenv("MINDNET_WP04_EDGE_TYPES", "references,belongs_to,prev,next")

@lru_cache
def get_settings() -> Settings:
    """Gibt die zentralen Einstellungen als Singleton zurück."""
    return Settings()