Predicting automatic triggers for obtaining code suggestions from a generative machine learning model

A method, comprising:

• receiving, via an interface of a development environment, one or more characters to add to a code file, wherein the one or more characters are associated with a user of the development environment;
• determining metadata describing a context for the received one or more characters and past code suggestion behavior for the user of the development environment;
• providing the metadata as input features for a machine learning model trained to predict whether to automatically trigger obtaining a code suggestion for the code file with the one or more added characters from a generative machine learning model, wherein a prediction of the machine learning model is to automatically trigger obtaining the code suggestion from the generative machine learning model;
• responsive to the prediction to automatically trigger obtaining the code suggestion:
• obtaining the code suggestion from the generative machine learning model based, at least in part, on the one or more characters;
• and
• providing the code suggestion via the interface of the development environment;
• and
• wherein the language machine learning model is hosted as part of a code development service offered by a provider network, and wherein the development environment implements a plugin that, upon addition to the development environment, enables the user to receive code suggestions from the code development service in the development environment.

One or more non-transitory, computer-readable storage media, storing program instructions that when executed on or across one or more computing devices cause the one or more computing devices to implement:

• responsive to receiving, via an interface of a development environment, one or more characters to add to a code file:
• determining metadata describing a context for the received one or more characters and past code suggestion behavior for a user associated with the development environment;
• providing the metadata as input to a machine learning model trained to predict whether to automatically trigger obtaining a code suggestion for the code file with the one or more added characters from a generative machine learning model;
• invoking the machine learning model to generate a prediction according to the metadata, wherein a prediction of the machine learning model is to automatically trigger obtaining the code suggestion from the generative machine learning model;
• responsive to the prediction to automatically trigger obtaining the code suggestion:
• obtaining the code suggestion from the generative machine learning model based, at least partly, on the one or more characters;
• and
• providing the code suggestion via the interface of the development environment;
• and
• wherein the language machine learning model is hosted as part of a code development service offered by a provider network, and wherein the program instructions implement a plugin for the development environment that, upon addition to the development environment, the plugin is configured to enable the user to receive code suggestions from the code development service in the development environment.