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from transformers import AutoTokenizer
from onnxruntime import InferenceSession
import numpy as np
import json
from fastapi import FastAPI
app = FastAPI()
# Initialize components
tokenizer = AutoTokenizer.from_pretrained(
"Xenova/multi-qa-mpnet-base-dot-v1",
use_fast=False # Avoids framework dependencies
)
session = InferenceSession("model.onnx")
def cosine_similarity(a, b):
return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
@app.post("/predict")
async def predict(query: str):
# Tokenize
inputs = tokenizer(query, return_tensors="np")
inputs = {k: v.astype(np.int64) for k, v in inputs.items()}
# Get embedding
embedding = session.run(None, inputs)[0][0]
return {"embedding": embedding.tolist()} |