70 lines
4.1 KiB
Python
70 lines
4.1 KiB
Python
"""
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Commander AI Lab — Coach Configuration
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────────────────────────────────────────
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Central config for Ollama, embeddings, and coach service paths.
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All paths are relative to the commander-ai-lab root directory.
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"""
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import os
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from pathlib import Path
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# ── Base Paths ───────────────────────────────────────────────────────
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LAB_ROOT = Path(os.environ.get("COMMANDER_LAB_ROOT", Path(__file__).parent.parent))
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# ── Ollama (OpenAI-compatible API) ────────────────────────────
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LLM_URL = os.environ.get("LLM_URL", "http://localhost:11434/v1")
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LLM_MODEL = os.environ.get("LLM_MODEL", "gpt-oss:20b")
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LLM_TIMEOUT = int(os.environ.get("LLM_TIMEOUT", "360")) # seconds
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LLM_MAX_RETRIES = 3
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# ── Embeddings ───────────────────────────────────────────────────
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EMBEDDINGS_DIR = LAB_ROOT / "data"
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EMBEDDINGS_NPZ = EMBEDDINGS_DIR / "mtg-embeddings.npz"
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EMBEDDINGS_PARQUET = EMBEDDINGS_DIR / "mtg-embeddings.parquet"
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# ── Deck Reports ────────────────────────────────────────────────
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DECK_REPORTS_DIR = LAB_ROOT / "deck-reports"
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COACH_SESSIONS_DIR = LAB_ROOT / "coach-sessions"
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# ── Prompt Limits ───────────────────────────────────────────────
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MAX_PROMPT_TOKENS = 2048
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MAX_CANDIDATES_PER_UNDERPERFORMER = 8
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MAX_UNDERPERFORMERS = 8
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UNDERPERFORMER_IMPACT_THRESHOLD = -0.05 # impactScore below this = underperformer
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# ── LLM Generation Settings (Coach — Ollama fallback) ─────────────
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DEFAULT_TEMPERATURE = 0.7
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DEFAULT_MAX_TOKENS = 16384
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# ── Anthropic Claude Deck Generation Settings ──────────────────────
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# Provider for deck generation: "anthropic" (Claude) or "local" (Ollama)
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DECK_GEN_PROVIDER = os.environ.get("DECK_GEN_PROVIDER", "anthropic")
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# Model used when DECK_GEN_PROVIDER="anthropic"
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DECK_GEN_MODEL = os.environ.get("DECK_GEN_MODEL", "claude-opus-4-20250514")
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# Anthropic API key
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ANTHROPIC_API_KEY = os.environ.get("ANTHROPIC_API_KEY", "")
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# Anthropic model for deck generation and coaching
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ANTHROPIC_MODEL = os.environ.get("ANTHROPIC_MODEL", "claude-opus-4-20250514")
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# Coach chat provider: "anthropic" (Claude API) or "local" (Ollama)
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COACH_PROVIDER = os.environ.get("COACH_PROVIDER", "anthropic")
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DECK_GEN_BASE_URL = os.environ.get("DECK_GEN_BASE_URL", "http://localhost:11434/v1")
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DECK_GEN_TEMPERATURE = float(os.environ.get("DECK_GEN_TEMPERATURE", "0.2"))
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DECK_GEN_MAX_TOKENS = int(os.environ.get("DECK_GEN_MAX_TOKENS", "16384"))
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# ── Smart Substitution Settings ───────────────────────────────────
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# Minimum embedding similarity to accept a substitute without Anthropic fallback
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SUBSTITUTION_MIN_SIMILARITY = float(os.environ.get("SUBSTITUTION_MIN_SIMILARITY", "0.75"))
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# Maximum number of alternatives to suggest per missing card
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SUBSTITUTION_MAX_ALTERNATIVES = int(os.environ.get("SUBSTITUTION_MAX_ALTERNATIVES", "5"))
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# Model for substitution fallback (claude-haiku-4-5 is fast + cheap for focused queries)
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SUBSTITUTION_MODEL = os.environ.get("SUBSTITUTION_MODEL", "claude-haiku-4-5")
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# Enable Anthropic fallback for low-confidence embedding matches
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SUBSTITUTION_USE_ANTHROPIC_FALLBACK = os.environ.get("SUBSTITUTION_USE_ANTHROPIC_FALLBACK", "true").lower() == "true"
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# ── Ensure directories exist ─────────────────────────────────────
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def ensure_dirs():
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"""Create required directories if they don't exist."""
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EMBEDDINGS_DIR.mkdir(parents=True, exist_ok=True)
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DECK_REPORTS_DIR.mkdir(parents=True, exist_ok=True)
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COACH_SESSIONS_DIR.mkdir(parents=True, exist_ok=True)
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