assets/script.js

import { VectorSearch } from '/assets/vectorsearch-min.js';

// DOM references.
const DB_NAME_INPUT = document.getElementById('db-name-input');
const DB_SELECT = document.getElementById('db-select');
const STATUS_EL = document.getElementById('status');
const QUERY_EMBEDDING_TEXT = document.getElementById('query-embedding-text');
const QUERY_TOKENS_OUTPUT = document.getElementById('query-tokens-output');
const QUERY_EMBEDDING_VIZ = document.getElementById('query-embedding-viz');
const BEST_MATCH_EMBEDDING_VIZ = document.getElementById('best-match-embedding-viz');
const BEST_MATCH_EMBEDDING_TEXT = document.getElementById('best-match-embedding-text');
const INPUT_TEXT = document.getElementById('input-text');
const TARGET_TEXT = document.getElementById('target-text');
const STORE_BTN = document.getElementById('store-btn');
const PREDICT_BTN = document.getElementById('predict-btn');
const THRESHOLD_INPUT = document.getElementById('threshold-input');
const THRESHOLD_VALUE = document.getElementById('threshold-value');
const RESULTS_TEXT = document.getElementById('results-text');
const SIMILARITY_CONTAINER = document.getElementById('similarity-container');
const SIMILARITY_SCORE_EL = document.getElementById('similarity-score');
const SIMILARITY_LABEL_EL = document.getElementById('similarity-label');


// Embedding Model Configuration.
const MODEL_RUNTIME = 'transformersjs'; // OPTIONS 'transformersjs' OR 'litertjs'
const MODEL_URL = 'huggingworld/all-MiniLM-L6-v2'; // OPTIONS 'Xenova/all-MiniLM-L6-v2' if transformersjs runtime or model/embeddinggemma-300M_seq1024_mixed-precision.tflite if litertjs runtime
const SEQ_LENGTH = 1024;
const TOKENIZER = 'huggingworld/embeddinggemma-300m-ONNX';
const EMBEDDING_MODEL_CONFIG = {
  runtime: MODEL_RUNTIME,
  url: MODEL_URL,
  sequenceLength: SEQ_LENGTH,
  tokenizer: TOKENIZER
};

// Instantiate VectorSearch Master Class.
const VECTOR_SEARCH = new VectorSearch(EMBEDDING_MODEL_CONFIG);


async function predictBtnClickHandler() {
  const QUERY_TEXT_VALUE = TARGET_TEXT.value;
  const THRESHOLD = parseFloat(THRESHOLD_INPUT.value) || 0.5;
  const SELECTED_DB = DB_SELECT.value;

  if (QUERY_TEXT_VALUE && SELECTED_DB) {
    VECTOR_SEARCH.setDb(SELECTED_DB);
    PREDICT_BTN.disabled = true;
    STATUS_EL.innerText = `Searching VectorDB (${SELECTED_DB})...`;
    const t0 = performance.now();
    await predict(QUERY_TEXT_VALUE, THRESHOLD);
    const t1 = performance.now();
    console.log(`Total search time (query embedding + vector search) took ${t1 - t0} milliseconds.`);
    STATUS_EL.innerText = 'Search complete';
    PREDICT_BTN.disabled = false;
  }
}


async function storeBtnClickHandler() {
  const text = INPUT_TEXT.value.trim();
  const dbName = DB_NAME_INPUT.value.trim();
  if (!text || !dbName) return;

  STORE_BTN.disabled = true;

  const paragraphs = text.split(/\n\s*\n/).map(p => p.trim()).filter(p => p.length > 0);

  await VECTOR_SEARCH.storeTexts(paragraphs, dbName, STATUS_EL);

  STATUS_EL.innerText = `Stored ${paragraphs.length} paragraphs.`;
  STORE_BTN.disabled = false;
  INPUT_TEXT.value = '';

  await updateDbList();
}


async function load() {
  try {
    await updateDbList();

    // Actually load the runtime and model so ready to use.
    await VECTOR_SEARCH.load(STATUS_EL);

    STATUS_EL.innerText = 'Ready to store and search';
    STORE_BTN.disabled = false;
    PREDICT_BTN.disabled = false;

    STORE_BTN.addEventListener('click', storeBtnClickHandler);
    PREDICT_BTN.addEventListener('click', predictBtnClickHandler);
    THRESHOLD_INPUT.addEventListener('input', () => {
      THRESHOLD_VALUE.innerText = THRESHOLD_INPUT.value;
    });
  } catch (e) {
    console.error(e);
    STATUS_EL.innerText = 'Error: ' + e.message;
  }
}


async function predict(queryText, threshold) {
  // Visualize embeddings and tokens for the search query text.
  const { embedding: EMBEDDING_DATA, tokens: TOKENS } = await VECTOR_SEARCH.getEmbedding(queryText);
  if (TOKENS) {
    VECTOR_SEARCH.renderTokens(TOKENS, QUERY_TOKENS_OUTPUT);
  }
  await VECTOR_SEARCH.renderEmbedding(EMBEDDING_DATA, QUERY_EMBEDDING_VIZ, QUERY_EMBEDDING_TEXT);

  // Now actually search the vector database.
  const { results: RESULTS, bestScore: BEST_SCORE, bestIndex: BEST_INDEX } = await VECTOR_SEARCH.search(EMBEDDING_DATA, threshold, DB_SELECT.value);

  if (RESULTS.length > 0) {
    RESULTS_TEXT.value = RESULTS.map(m => `[Score: ${m.score.toFixed(4)}]\n${m.text}`).join('\n\n');
    updateSimilarityUI(BEST_SCORE);

    const bestMatchVector = RESULTS[BEST_INDEX].vector;
    if (bestMatchVector) {
      await VECTOR_SEARCH.renderEmbedding(bestMatchVector, BEST_MATCH_EMBEDDING_VIZ, BEST_MATCH_EMBEDDING_TEXT);
    }
  } else {
    RESULTS_TEXT.value = "No matches found above threshold.";
    SIMILARITY_CONTAINER.classList.add('hidden');
    BEST_MATCH_EMBEDDING_VIZ.innerHTML = '';
    BEST_MATCH_EMBEDDING_TEXT.innerText = '';
  }
}


function updateSimilarityUI(score) {
  SIMILARITY_CONTAINER.classList.remove('hidden');
  SIMILARITY_SCORE_EL.innerText = score.toFixed(4);

  const HUE = Math.max(0, Math.min(120, score * 120));
  const BACKGROUND_COLOUR = `hsla(${HUE}, 70%, 20%, 0.4)`;
  const BORDER_COLOUR = `hsla(${HUE}, 70%, 50%, 0.6)`;

  SIMILARITY_CONTAINER.style.backgroundColor = BACKGROUND_COLOUR;
  SIMILARITY_CONTAINER.style.borderColor = BORDER_COLOUR;

  let label = 'Low Similarity';
  if (score > 0.8) {
    label = 'Very High Similarity';
  } else if (score > 0.6) {
    label = 'High Similarity';
  } else if (score > 0.4) {
    label = 'Moderate Similarity';
  }

  SIMILARITY_LABEL_EL.innerText = label;
}


async function updateDbList() {
  if (!window.indexedDB.databases) {
    console.warn('indexedDB.databases() is not supported in this browser.');
    return;
  }

  try {
    const dbs = await window.indexedDB.databases();
    const currentSelection = DB_SELECT.value;

    DB_SELECT.innerHTML = '';
    const currentInputName = DB_NAME_INPUT.value.trim();
    let names = dbs.map(db => db.name).filter(name => name !== undefined);

    if (currentInputName && !names.includes(currentInputName)) {
      names.push(currentInputName);
    }

    names.sort();

    names.forEach(name => {
      const option = document.createElement('option');
      option.value = name;
      option.text = name;
      if (name === currentSelection || (currentSelection === '' && name === currentInputName)) {
        option.selected = true;
      }
      DB_SELECT.appendChild(option);
    });
  } catch (e) {
    console.error('Error fetching databases:', e);
  }
}


load();