// hosted on supabase storage
const session = await RawSession.fromStorage('models/model.onnx')
// or from hugging face repo
const session = await RawSession.fromHuggingFace('Supabase/gte-small');
// or using the model file url direclty
const session = await RawSession.fromUrl("https://example.com/model.onnx");

const session = await RawSession.fromUrl("https://example.com/model.onnx");

// Prepare the input tensors
const inputs = {
  input1: new RawTensor("float32", [1.0, 2.0, 3.0], [1, 3]),
  input2: new RawTensor("float32", [4.0, 5.0, 6.0], [1, 3]),
};

const outputs = await session.run(inputs);
console.log(outputs.output1); // Output tensor

import { Tensor } from "@huggingface/transformers.js";
const { RawTensor, RawSession } = Supabase.ai;
   
const session = await RawSession.fromHuggingFace('Supabase/gte-small');
   
// Example only, in real 'feature-extraction' tensors are given from the tokenizer step. 
// Consider 'n' as the batch size
const inputs = {
   input_ids: new RawTensor('float32', [1, 2, 3...], [n, 2]),
   attention_mask: new RawTensor('float32', [...], [n, 2]),
   // @ts-ignore: mixing Tensors from both
   token_types_ids: new Tensor('float32', [...], [n, 2])
};
   
const { last_hidden_state } = await session.run(inputs);
   
// Using `transformers.js` APIs
const hfTensor = Tensor.mean_pooling(last_hidden_state, inputs.attention_mask).normalize();
   
return hfTensor.tolist();

const { RawTensor, RawSession } = Supabase.ai;

// Custom filename on Hugging Face, default: 'model_quantized.onnx'
const session = await RawSession.fromStorage('models/vehicle-emission.onnx');

Deno.serve(async (req: Request) => {
  const carsBatchInput = await req.json();

  // Parsing objects to tensor input
  const inputTensors = {};
  session.inputs.forEach((inputKey) => {
    const values = carsBatchInput.map((item) => item[inputKey]);

    // This model uses `float32` tensors, but could variate to mixed types
    inputTensors[inputKey] = new RawTensor('float32', values, [values.length, 1]);
  });

  const { emissions } = await session.run(inputTensors);

  return Response.json({ result: emissions });  // [ 289.01, 199.53]
});