A computer-implemented data generation method based on a deep learning model, wherein the deep learning model is able to generate reply data based on input data of a user, and the data generation method comprises: determining an initial input for the deep learning model based on input data from a user; obtaining a first output of the deep learning model, wherein in response to the deep learning model determining that generating a reply based on the initial input requires calling a first functional component different from the deep learning model, the first output comprises a first token for calling the first functional component and a first intermediate inquiry determined based on the initial input and recognizable by the first functional component; obtaining a first intermediate result determined by the first functional component based on the first intermediate inquiry; determining a second input for the deep learning model based at least on the initial input and the first intermediate result; and obtaining a second output of the deep learning model for generating the reply to the initial input, characterised in that, the first functional component is an external memory bank, and the external memory bank stores a first data group set related to the user, and wherein each data group in the first data group set comprises at least a historical input data item and a historical reply item generated by the deep learning model for the historical input data item, wherein the first intermediate inquiry is based on the input data, and wherein the first intermediate result is a historical reply item corresponding to a historical input data item that is in the first data group set and whose similarity with the input data is higher than a first threshold. determining an initial input for the deep learning model based on input data from a user; obtaining a first output of the deep learning model, wherein in response to the deep learning model determining that generating a reply based on the initial input requires calling a first functional component different from the deep learning model, the first output comprises a first token for calling the first functional component and a first intermediate inquiry determined based on the initial input and recognizable by the first functional component; obtaining a first intermediate result determined by the first functional component based on the first intermediate inquiry; determining a second input for the deep learning model based at least on the initial input and the first intermediate result; and obtaining a second output of the deep learning model for generating the reply to the initial input, characterised in that, the first functional component is an external memory bank, and the external memory bank stores a first data group set related to the user, and wherein each data group in the first data group set comprises at least a historical input data item and a historical reply item generated by the deep learning model for the historical input data item, wherein the first intermediate inquiry is based on the input data, and wherein the first intermediate result is a historical reply item corresponding to a historical input data item that is in the first data group set and whose similarity with the input data is higher than a first threshold.