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Sentence Transformer embeddings for semantic similarity.
Uses MPS (Apple Silicon GPU) when available.
"""
import numpy as np
_embedding_model = None
def get_embedding_model(device: str = None):
"""Load Sentence Transformer model (cached singleton)."""
global _embedding_model
if _embedding_model is not None:
return _embedding_model
try:
from sentence_transformers import SentenceTransformer
import torch
if device is None:
device = "mps" if torch.backends.mps.is_available() else "cpu"
_embedding_model = SentenceTransformer("all-MiniLM-L6-v2", device=device)
return _embedding_model
except ImportError:
return None
def embedding_cosine_similarity(q1: str, q2: str, model=None) -> float:
"""
Compute cosine similarity between question embeddings.
Returns 0.0 if model unavailable.
"""
if model is None:
model = get_embedding_model()
if model is None:
return 0.0
embeddings = model.encode([q1, q2])
a, b = embeddings[0], embeddings[1]
return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b) + 1e-9))
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