To update and retrain LlamaIndex with new data, you first need to ensure you have the latest version of your dataset that you intend to add. Depending on your specific application, this data may come from various sources, such as updated documents, user inputs, or other databases. Once you have this new data ready, the process generally involves two main steps: updating the index and retraining the model.
The first step is to update the index. This is done by adding the new data to your existing LlamaIndex setup. If you are working with a framework that allows for dynamic indexing, you can simply append the new data to the index. This typically involves using a function or method provided by the LlamaIndex API designed for this purpose. For instance, if the new data is in JSON format, you might use a method like index.add_documents(new_data) where new_data is your updated dataset. After this process, it’s crucial to validate that the index has been updated correctly, which you can do by performing a few test queries to ensure the new information is retrievable and relevant.
The second step is retraining the model to integrate the newly added data. This is an essential part of the updating process because it helps the model learn from the new information and improve its accuracy over time. To retrain, you would typically load the updated LlamaIndex into your training pipeline and run the training process again. You might need to specify parameters like learning rate, batch size, and number of epochs to optimize performance. After retraining, it's a good practice to evaluate the model using validation data to check if it has improved and is making relevant predictions based on the updated data. By following these steps, you can keep your LlamaIndex current and ensure it leverages the most recent information available.