Implementing Custom Knowledge Retrieval for RAG with TensorFlow

Present a code snippet for implementing a custom knowledge retrieval component to pair with RAG for TensorFlow-based applications, enabling refined control over the information sources.
<!DOCTYPE html>
<html lang="en">
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Custom Knowledge Retrieval for RAG</title>
<script src=""></script>
  #result { padding: 10px; border: 1px solid #ccc; margin-top: 10px; }

  <input type="text" id="query" placeholder="Enter your query">
  <button id="retrieve">Retrieve Knowledge</button>
<div id="result"></div>

  // Your JavaScript code will go here

This is the HTML structure providing a basic interface with an input field for a user's query and a button to trigger the knowledge retrieval process. It also includes TensorFlow.js CDN link and minimal styling for results display.
document.getElementById('retrieve').addEventListener('click', async function() {
  const query = document.getElementById('query').value;
  const resultElement = document.getElementById('result');
  // Implement custom knowledge retrieval using TensorFlow and RAG.
  // Find or load the RAG model
  // const ragModel = await loadRagModel();
  // const knowledge = await ragModel.retrieveKnowledge(query);
  // For simplification, let's mock the retrieved knowledge
  const knowledge = "This is a piece of retrieved knowledge based on the query: " + query;
  // Display results
  resultElement.textContent = knowledge;
This JavaScript code snippet binds an event listener to the 'Retrieve Knowledge' button, which on click retrieves the user's query, mocks a knowledge retrieval process, and displays the results. Integration with a real RAG model would involve loading the model and retrieving relevant information using TensorFlow.js.