embedder.py

# embedder.py
from sentence_transformers import SentenceTransformer
from typing import List
import threading
import json

# Single global model instance (thread-safe lazy init)
_model = None
_lock = threading.Lock()

def _load_config():
    with open("config.json", "r", encoding="utf-8") as f:
        return json.load(f)

def get_model():
    global _model
    if _model is None:
        with _lock:
            if _model is None:
                cfg = _load_config()
                name = cfg["rag"]["embedding_model"]
                _model = SentenceTransformer(name)
    return _model

def embed_texts(texts: List[str]):
    model = get_model()
    return model.encode(texts, normalize_embeddings=True).tolist()

def embed_text(text: str):
    return embed_texts([text])[0]