"""
Commander AI Lab — Coach Configuration
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Central config for Ollama, embeddings, and coach service paths.
All paths are relative to the commander-ai-lab root directory.
"""

import os
from pathlib import Path

# ── Base Paths ───────────────────────────────────────────────────────
LAB_ROOT = Path(os.environ.get("COMMANDER_LAB_ROOT", Path(__file__).parent.parent))

# ── Ollama (OpenAI-compatible API) ────────────────────────────
LLM_URL = os.environ.get("LLM_URL", "http://localhost:11434/v1")
LLM_MODEL = os.environ.get("LLM_MODEL", "gpt-oss:20b")
LLM_TIMEOUT = int(os.environ.get("LLM_TIMEOUT", "360"))  # seconds
LLM_MAX_RETRIES = 3

# ── Embeddings ───────────────────────────────────────────────────
EMBEDDINGS_DIR = LAB_ROOT / "data"
EMBEDDINGS_NPZ = EMBEDDINGS_DIR / "mtg-embeddings.npz"
EMBEDDINGS_PARQUET = EMBEDDINGS_DIR / "mtg-embeddings.parquet"

# ── Deck Reports ────────────────────────────────────────────────
DECK_REPORTS_DIR = LAB_ROOT / "deck-reports"
COACH_SESSIONS_DIR = LAB_ROOT / "coach-sessions"

# ── Prompt Limits ───────────────────────────────────────────────
MAX_PROMPT_TOKENS = 2048
MAX_CANDIDATES_PER_UNDERPERFORMER = 8
MAX_UNDERPERFORMERS =  8
UNDERPERFORMER_IMPACT_THRESHOLD = -0.05  # impactScore below this = underperformer

# ── LLM Generation Settings (Coach — Ollama fallback) ─────────────
DEFAULT_TEMPERATURE = 0.7
DEFAULT_MAX_TOKENS = 16384

# ── Anthropic Claude Deck Generation Settings ──────────────────────
# Provider for deck generation: "anthropic" (Claude) or "local" (Ollama)
DECK_GEN_PROVIDER = os.environ.get("DECK_GEN_PROVIDER", "anthropic")
# Model used when DECK_GEN_PROVIDER="anthropic"
DECK_GEN_MODEL = os.environ.get("DECK_GEN_MODEL", "claude-opus-4-20250514")
# Anthropic API key
ANTHROPIC_API_KEY = os.environ.get("ANTHROPIC_API_KEY", "")
# Anthropic model for deck generation and coaching
ANTHROPIC_MODEL = os.environ.get("ANTHROPIC_MODEL", "claude-opus-4-20250514")
# Coach chat provider: "anthropic" (Claude API) or "local" (Ollama)
COACH_PROVIDER = os.environ.get("COACH_PROVIDER", "anthropic")
DECK_GEN_BASE_URL = os.environ.get("DECK_GEN_BASE_URL", "http://localhost:11434/v1")
DECK_GEN_TEMPERATURE = float(os.environ.get("DECK_GEN_TEMPERATURE", "0.2"))
DECK_GEN_MAX_TOKENS = int(os.environ.get("DECK_GEN_MAX_TOKENS", "16384"))

# ── Smart Substitution Settings ───────────────────────────────────
# Minimum embedding similarity to accept a substitute without Anthropic fallback
SUBSTITUTION_MIN_SIMILARITY = float(os.environ.get("SUBSTITUTION_MIN_SIMILARITY", "0.75"))
# Maximum number of alternatives to suggest per missing card
SUBSTITUTION_MAX_ALTERNATIVES = int(os.environ.get("SUBSTITUTION_MAX_ALTERNATIVES", "5"))
# Model for substitution fallback (claude-haiku-4-5 is fast + cheap for focused queries)
SUBSTITUTION_MODEL = os.environ.get("SUBSTITUTION_MODEL", "claude-haiku-4-5")
# Enable Anthropic fallback for low-confidence embedding matches
SUBSTITUTION_USE_ANTHROPIC_FALLBACK = os.environ.get("SUBSTITUTION_USE_ANTHROPIC_FALLBACK", "true").lower() == "true"

# ── Ensure directories exist ─────────────────────────────────────
def ensure_dirs():
    """Create required directories if they don't exist."""
    EMBEDDINGS_DIR.mkdir(parents=True, exist_ok=True)
    DECK_REPORTS_DIR.mkdir(parents=True, exist_ok=True)
    COACH_SESSIONS_DIR.mkdir(parents=True, exist_ok=True)