"""
Embedding utilities for memory service.

Provides text-to-vector embedding generation and similarity calculations.
"""
import numpy as np
from sentence_transformers import SentenceTransformer
from core.logger import setup_logger

logger = setup_logger('embeddings', service_name='memory_service')

# Initialize sentence transformer model (loaded once at module import)
_model = None


def get_model() -> SentenceTransformer:
    """Get or initialize the sentence transformer model"""
    global _model
    if _model is None:
        logger.info("[μ] Loading sentence transformer model: all-MiniLM-L6-v2")
        _model = SentenceTransformer('all-MiniLM-L6-v2')
        logger.info("[μ] Sentence transformer model loaded successfully")
    return _model


def generate_embedding(text: str) -> np.ndarray:
    """
    Generate semantic embedding for text.

    Args:
        text: Input text to embed

    Returns:
        Normalized embedding vector as numpy array
    """
    model = get_model()
    return np.array(model.encode(text, normalize_embeddings=True))


def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
    """
    Calculate cosine similarity between two vectors.

    Args:
        a: First embedding vector
        b: Second embedding vector

    Returns:
        Similarity score between 0.0 and 1.0
    """
    if np.linalg.norm(a) == 0 or np.linalg.norm(b) == 0:
        return 0.0
    return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)))