Abstract: The described examples offer a solution by providing a design-time environment and a run-time environment. The design-time environment helps developers create and maintain configuration and other information that can be formed into chatbot runtime capabilities more easily. Developers can define what data can be accessed and how the data is to be filtered or displayed. The run-time environment is where the actual interaction with the user happens. When a user inputs a query, the system translates the query into a structured format that the ERP system can understand. This involves several steps, including identifying a capability that matches the query, generating a prompt for a large language model (using the capability), receiving a query, executing the query, and then processing the results to present them to the user.

Abstract: Data is accessed that encapsulates a corpus of text. Thereafter, at least a portion of the corpus of text is input into an ensemble of machine learning models comprising a convolutional neural network, a long short-term memory network and a graph convolutional network to extract a plurality of features and to define relationships amongst the entities. Data encapsulating the entities and their relationships within the corpus of text are then received from an output layer of the ensemble of machine learning models. Related apparatus, systems, techniques and articles are also described.

Abstract: Systems and methods provide acquisition of a plurality of code artifacts and one or more code review comments associated with each code artifact, generation of a set of code features based on each of the plurality of code artifacts, input of each set of code features to a neural network to generate code review comments respectively associated with each of the plurality of code artifacts, determination of a loss by comparing each generated code review comment respectively associated with one of plurality of code artifacts with the one or more review comments associated with the one of plurality of code artifacts, and modification of the neural network based on the loss.