config.py

import os

# --- Architecture Constants ---
NUM_CLUSTERS = 32
FRESHNESS_SHARD_ID = 999
MRL_DIMS = 64

# --- Qdrant Configuration ---
# Use in-memory for testing if QDRANT_URL is not set, otherwise connect to cloud/local instance
QDRANT_URL = os.getenv("QDRANT_URL", "https://justmotes-xvector-db-node.hf.space") 
QDRANT_API_KEY = os.getenv("QDRANT_API_KEY", "xvector_secret_pass_123")
COLLECTION_NAME = "dashVector_v1"

# --- Model Configurations ---
EMBEDDING_MODELS = {
    "minilm": "sentence-transformers/all-MiniLM-L6-v2",  # Baseline (384 dims)
    "nomic": "nomic-ai/nomic-embed-text-v1.5",           # Primary, MRL-capable (768 dims, matryoshka compatible)
    "qwen": "Alibaba-NLP/gte-Qwen2-1.5B-instruct"        # SOTA (1536 dims)
}

ROUTER_MODELS = ["lightgbm", "logistic", "mlp"]

# --- Paths ---
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
LOGS_DIR = os.path.join(BASE_DIR, "logs")
ACTIVE_LEARNING_LOG = os.path.join(LOGS_DIR, "active_learning_queue.jsonl")

# Ensure logs directory exists
os.makedirs(LOGS_DIR, exist_ok=True)