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path: root/semanticsearchscratchpad/index.html
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 <div class="controls" tabindex="0">
      <form>
        <div>
          <label for="number1">Multiply number 1: </label>
          <input type="text" id="number1" value="0" />
        </div>
        <div>
          <label for="number2">Multiply number 2: </label>
          <input type="text" id="number2" value="0" />
        </div>
      </form>

      <pre id="result"></pre>
    </div>

    <!--    <script src="dataset.js"></script> --!>
<script type="module">
import {Stripe_1,
Stripe_2,
Stripe_3,
Gmail_1,
Gmail_2,
Gmail_3,
Alexa_1,
Alexa_2,
Alexa_3
} from './dataset.js';

import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected]';
env.allowLocalModels = false;
const extractor = await pipeline('embeddings', 'Xenova/all-MiniLM-L6-v2');
const emb = x => extractor(x, {pooling:'mean', normalize:'true'});


const s = {Stripe_1,
Stripe_2,
Stripe_3,
Gmail_1,
Gmail_2,
Gmail_3,  
Alexa_1, 
Alexa_2, 
Alexa_3};


//import genE from './create-embeddings.js';

//genE({data: {group:'Stripe_1', dataset: Stripe_1}});

Object.keys(s).map(k => {
const embWorker = new Worker("create-embeddings.js");
//const k = 'Gmail_1';
embWorker.onmessage = ({data}) => {
    if (data.loaded) {
        embWorker.postMessage({group:k, dataset: s[k]});
        console.log('Message posted to worker', {group:k, dataset: s[k]});
    } else {
        console.error(data);
    }
}
});



//let pipe = await pipeline('embeddings', 'Xenova/all-MiniLM-L6-v2');
//
//let emb = x => pipe(x, {pooling:'mean', normalize:'true'});
//
//async function createEmbeddings(xs) {
//    return (await Promise.all(xs.map(emb))).map((x,i) => ({value: xs[i], embeddings: x.data}));
//}

function dotp(x, y) {
  function dotp_sum(a, b) {
    return a + b;
  }
  function dotp_times(_, i) {
    return x[i] * y[i];
  }
  return x.map(dotp_times).reduce((a, v) => a + v, 0);
}

function cosineSimilarity(A,B){
  var similarity = dotp(A, B) / (Math.sqrt(dotp(A,A)) * Math.sqrt(dotp(B,B)));
  return similarity;
}



//const yemb = (await emb('I really like curry.')).data;
//
//
//console.log('out', xsembs.map(x => ({value:x.value, similarity: cosineSimilarity(x.embeddings, yemb)})));
//

//const request = indexedDB.open("embeddings");
//request.onerror = (event) => {
//  console.error("Why didn't you allow my web app to use IndexedDB?!");
//};
//
//request.onupgradeneeded = (event) => {
//  console.log('onupgradeneeded')
//  const db = event.target.result;
//  const objectStore = db.createObjectStore('embeddings', {autoIncrement: true});
//  objectStore.createIndex("value", "value", { unique: false });
//  };
//
var embeddings;

number1.onchange = e => {
    const query = e.target.value;
  const request = indexedDB.open("embeddings");
  request.onsuccess = (event) => {
    const db = event.target.result;
    const t = db.transaction('embeddings', 'readwrite').objectStore('embeddings').getAll();
    t.onsuccess = e => {
  //    embeddings.onsuccess =  =>
     embeddings = e.target.result;
      if (embeddings && embeddings.length) {    
            emb(query).then(yemb =>{
            const r = embeddings
              .map(x => ({value:x.value, similarity: cosineSimilarity(x.embeddings, yemb.data)}));
            result.textContent = r
              .sort((a,b) => b.similarity - a.similarity)
              .slice(0, 10)
              .map(({value, similarity}) => `${similarity}: ${value}`)
              .join('\n');
              }
              );
      } else {
          console.error(embeddings);
      }
    }
  }
}

//request.onsuccess = (event) => {
//  const db = event.target.result;
//  let t = db.transaction('embeddings', 'readwrite').objectStore('embeddings').getAll();
//  t.onerror = () => console.error('transaction failed');
//  t.onsuccess = e => {
//    const embeddings = e.target.result;
//    if (!embeddings || !embeddings.length) {
//        createEmbeddings(ds)
//      .then(xsembs => {
//        const st = db
//            .transaction('embeddings', 'readwrite')
//            .objectStore('embeddings');
//        xsembs.forEach(emb => {
//            console.log(emb);
//            st.add(emb);
//        });
//      });
//    }
//}
//}




//request.onsuccess = (event) => {
//  console.log('onsuccess')
//  const db = event.target.result;
//  const st = db
//      .transaction('embeddings', 'readwrite')
//      .objectStore('embeddings');
//  xsembs.forEach(emb => {
//      console.log(emb);
//      st.add(emb);
//  });
//};

/******/

const first = document.querySelector('#number1');
const second = document.querySelector('#number2');


const myWorker = new Worker("worker.js");

//first.onchange = function() {
//  myWorker.postMessage([first.value, second.value]);
//  console.log('Message posted to worker', [first.value, second.value]);
//}

second.onchange = function() {
  myWorker.postMessage([first.value, second.value]);
  console.log('Message posted to worker', [first.value, second.value]);
}

myWorker.onmessage = function(e) {
  result.textContent = e.data;
  console.log('Message received from worker', e.data);
}

</script>