"""
Centralized project configuration.

The single place where the following are defined: artifact paths, model names,
search parameters and session constants. All other modules import from here,
avoiding hardcoded values scattered throughout the code.
"""

import os
from dotenv import load_dotenv

# Load environment variables from .env (API keys)
load_dotenv()

# --- Project Paths ---
# Goes up two levels: utils/config.py → utils/ → project root
PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))

# --- Artifact Paths ---
ARTIFACTS_DIR = os.path.join(PROJECT_ROOT, "artifacts")
FAISS_PRODUCT_INDEX = os.path.join(ARTIFACTS_DIR, "faiss_products")      # FAISS catalog index (100k products)
FAISS_ROUTER_INDEX = os.path.join(ARTIFACTS_DIR, "faiss_router")         # FAISS router index (few-shot examples)
RECOMMENDER_MODEL_PATH = os.path.join(ARTIFACTS_DIR, "recommender_model.pkl")  # Hybrid LightFM model
METADATA_PATH = os.path.join(ARTIFACTS_DIR, "metadata_cleaned.json")     # Product metadata (ASIN, title, price, etc.)
IMAGES_DIR = os.path.join(ARTIFACTS_DIR, "images")                       # Product images downloaded from Amazon
FAISS_VISUAL_INDEX = os.path.join(ARTIFACTS_DIR, "faiss_visual")         # FAISS visual embedding index (DinoV2 256d)

# --- Models ---
EMBEDDING_MODEL_NAME = "paraphrase-multilingual-MiniLM-L12-v2"  # Multilingual sentence-transformer (IT/EN same vector space)
LLM_MODEL_NAME = "gpt-4o-mini"             # LLM for router, translation and responses
LLM_TEMPERATURE = 0                        # Deterministic (no variability)
VISUAL_MODEL_NAME = "Trendyol/trendyol-dino-v2-ecommerce-256d"  # DinoV2 fine-tuned for e-commerce (256d)

# --- Response Language ---
# Language of the bot's user-facing replies: "en" (default) or "it".
# Input queries are accepted in ANY language and translated to English internally
# for retrieval — this setting only controls the OUTPUT language. See utils/i18n.py.
RESPONSE_LANG = os.getenv("RESPONSE_LANG", "en")

# --- Search Parameters ---
PRODUCT_SEARCH_K = 12   # How many products the Search Agent returns (3 rows of 4)
RECOMMENDER_K = 8       # How many products the Recommender Agent recommends
ROUTER_SEARCH_K = 15    # How many few-shot examples for distance-weighted kNN voting
VISUAL_SEARCH_K = 8     # How many visually similar products the Visual Agent returns

# --- Session ---
DEFAULT_THREAD_ID = "memorysaver_id"  # Session ID for LangGraph's MemorySaver