# rag/models/embedder.py
from typing import List
import numpy as np
import onnxruntime as ort
from fastapi import Request

def _l2_normalize(vec: np.ndarray) -> List[float]:
    norm = np.linalg.norm(vec) or 1.0
    return (vec / norm).tolist()

def get_embedding(request: Request, text: str) -> List[float]:
    """
    request.app.state.embedder_sess : ONNX Runtime InferenceSession
    request.app.state.embedder_tokenizer : 토크나이저
    """
    tokenizer = request.app.state.embedder_tokenizer
    sess: ort.InferenceSession = request.app.state.embedder_sess

    inputs = tokenizer(text, return_tensors="np", padding=True, truncation=True, max_length=256)
    ort_inputs = {k: v for k, v in inputs.items()}
    ort_outs = sess.run(None, ort_inputs)
    # 일반적으로 첫 번째 출력이 [batch, dim] 임베딩
    vec = ort_outs[0][0]
    return _l2_normalize(vec)