Integrating RAG with TensorFlow for Question Answering

Demonstrate the integration of RAG with a TensorFlow backend for creating an advanced question-answering system that retrieves context before generating an answer.

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>RAG Question Answering with TensorFlow</title>
    <style>
        body { font-family: Arial, sans-serif; }
        #question { width: 300px; }
        #answer { margin-top: 20px; }
    </style>
</head>
<body>
    <h1>RAG-TensorFlow QA System</h1>
    <input type="text" id="question" placeholder="Type your question...">
    <button onclick="getAnswer()">Ask</button>
    <div id="answer"></div>
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
    <script src="path-to-your-rag-model.js"></script>
    <script>
        async function getAnswer() {
            const questionElement = document.getElementById('question');
            const answerElement = document.getElementById('answer');
            const question = questionElement.value;
            const answer = await generateAnswer(question);
            answerElement.textContent = 'Answer: ' + answer;
        }
    </script>
</body>
</html>

This HTML page includes styling for a simple text input and answer display area. It references TensorFlow.js for the neural network computation and a separate JS file for the RAG model. It defines a function, getAnswer(), that is called when the 'Ask' button is pressed and uses the RAG model to generate an answer from the given question.

async function generateAnswer(question) {
    // Load the RAG model; this needs to be adjusted according to where and how the model is stored.
    const model = await tf.loadLayersModel('path-to-your-rag-model/model.json');

    // Preprocess the question into the format expected by the model (this will vary by model)
    const inputTensor = tf.tensor(preprocessQuestion(question));

    // Predict the answer using the RAG model
    const prediction = model.predict(inputTensor);

    // Post-process the model output to readable text
    const answer = postprocessAnswer(prediction);
    return answer;
}

This JavaScript function, generateAnswer, loads the RAG model using TensorFlow.js, preprocesses the question input to a tensor, uses the model to predict the answer, and then postprocesses the answer. Details like 'preprocessQuestion' and 'postprocessAnswer' are placeholders for actual functions that will encode the question for input and decode the model's output into a human-readable answer.