To periodically update the KNN model, you can use the following steps:

1. Define a time interval for updating the model:

```

const updateInterval = 60 * 1000; // Update model every 60 seconds

```

2. Use the `setInterval` method to periodically update the KNN model:

```

setInterval(() => {

// Update KNN model here

}, updateInterval);

```

3. Inside the `setInterval` callback function, you can update the KNN model by retraining it with new data or removing old data points:

```

setInterval(() => {

// Remove examples that are older than a certain time threshold

const threshold = Date.now() - (60 * 60 * 1000); // Remove examples older than 1 hour

const examples = knn.getClassExampleCount();

Object.keys(examples).forEach(label => {

const exampleCount = examples[label];

if (exampleCount > 0) {

const latestExample = knn.getClassExample(label, exampleCount - 1);

if (latestExample.timestamp < threshold) {

knn.removeExample(label, latestExample);

}

}

});

// Retrain KNN model with new data

const newData = ['Howdy', 'Nice to meet you'];

newData.forEach(async (data, index) => {

const embedding = await model.embed(data).array();

const tensor = tf.tensor1d(embedding);

knn.addExample(tensor, index);

});

}, updateInterval);

```

In the above code, we remove examples from the KNN model that are older than a certain time threshold. We then retrain the KNN model with new data by preprocessing the data, creating tensors, and adding them to the KNN model using `addExample`.

Note that the above code is just an example and you may need to adjust the time threshold and update interval to suit your specific use case.