Abstract: Methods, systems, and computer programs are presented for implementing a tool that generates and executes the code in a controlled environment to satisfy user requests entered as text prompts. One method includes receiving a user prompt with a user request received in a user interface (UI), generating a first Large Language Model (LLM) prompt to create a plan, and receiving the plan from an LLM, the plan comprising text describing a sequence of operations. The method further includes generating a second LLM prompt, to create code, specifying which code instructions are permitted in the created code. Further, the method receives the code from the LLM that received the second LLM prompt, and executes the code in a controlled environment. Further, the results generated by executing the code are presented in the UI.

Abstract: Methods, systems, and computer programs are presented for implementing a tool that generates and executes the code in a controlled environment to satisfy user requests entered as text prompts. One method includes receiving a user prompt with a user request received in a user interface (UI), generating a first Large Language Model (LLM) prompt to create a plan, and receiving the plan from an LLM, the plan comprising text describing a sequence of operations. The method further includes generating a second LLM prompt, to create code, specifying which code instructions are permitted in the created code. Further, the method receives the code from the LLM that received the second LLM prompt, and executes the code in a controlled environment. Further, the results generated by executing the code are presented in the UI.

Abstract: Methods, systems, and computer programs are presented for implementing a tool that generates and executes the code in a controlled environment to satisfy user requests entered as text prompts. One method includes receiving a user prompt with a user request received in a user interface (UI), generating a first Large Language Model (LLM) prompt to create a plan, and receiving the plan from an LLM, the plan comprising text describing a sequence of operations. The method further includes generating a second LLM prompt, to create code, specifying which code instructions are permitted in the created code. Further, the method receives the code from the LLM that received the second LLM prompt, and executes the code in a controlled environment. Further, the results generated by executing the code are presented in the UI.

Abstract: Techniques are presented for managing access to external resources while executing code. The method involves receiving a request to execute code and sending an identifier (ID) for the code execution and credentials for accessing an external resource to a trusted server. The request is sent to execute the code with the ID to a code executer, and the code is executed. During code execution, an operation for accessing the external resource is detected. In response to the detection, a request to access the external resource is sent from the code executer to the trusted server. The trusted server accesses the external resource using the provided credentials and sends a response with the results back to the code executer. The code executer does not have access to the credentials for accessing the external resource. After the code terminates execution, the results from the code execution are presented on a device.

Abstract: Techniques are presented for managing access to external resources while executing code. The method involves receiving a request to execute code and sending an identifier (ID) for the code execution and credentials for accessing an external resource to a trusted server. The request is sent to execute the code with the ID to a code executer, and the code is executed. During code execution, an operation for accessing the external resource is detected. In response to the detection, a request to access the external resource is sent from the code executer to the trusted server. The trusted server accesses the external resource using the provided credentials and sends a response with the results back to the code executer. The code executer does not have access to the credentials for accessing the external resource. After the code terminates execution, the results from the code execution are presented on a device.

Abstract: Methods, systems, and computer programs are presented for implementing a tool that generates and executes the code in a controlled environment to satisfy user requests entered as text prompts. One method includes receiving a user prompt with a user request received in a user interface (UI), generating a first Large Language Model (LLM) prompt to create a plan, and receiving the plan from an LLM, the plan comprising text describing a sequence of operations. The method further includes generating a second LLM prompt, to create code, specifying which code instructions are permitted in the created code. Further, the method receives the code from the LLM that received the second LLM prompt, and executes the code in a controlled environment. Further, the results generated by executing the code are presented in the UI.

Abstract: Methods, systems, and computer programs are presented for implementing a tool that generates and executes the code in a controlled environment to satisfy user requests entered as text prompts. One method includes receiving a user prompt with a user request received in a user interface (UI), generating a first Large Language Model (LLM) prompt to create a plan, and receiving the plan from an LLM, the plan comprising text describing a sequence of operations. The method further includes generating a second LLM prompt, to create code, specifying which code instructions are permitted in the created code. Further, the method receives the code from the LLM that received the second LLM prompt, and executes the code in a controlled environment. Further, the results generated by executing the code are presented in the UI.

Abstract: Methods, systems, and computer programs are presented for implementing a tool that generates and executes the code in a controlled environment to satisfy user requests entered as text prompts. One method includes receiving a user prompt with a user request received in a user interface (UI), generating a first Large Language Model (LLM) prompt to create a plan, and receiving the plan from an LLM, the plan comprising text describing a sequence of operations. The method further includes generating a second LLM prompt, to create code, specifying which code instructions are permitted in the created code. Further, the method receives the code from the LLM that received the second LLM prompt, and executes the code in a controlled environment. Further, the results generated by executing the code are presented in the UI.

Abstract: Techniques described herein provide a service and associated interface that are capable of serving online queries for learners' states with respect to various types of learning content. For example, the described techniques may serve a query to determine items that have been completed by a particular user, or courses with which a user has recently interacted. More particularly, the described techniques provide a uniform interface for all such learner progress, which facilitates fast and efficient capabilities of users, administrators, and developers with respect to finding, using, and modifying learner progress.

Abstract: Techniques described herein provide a service and associated interface that are capable of serving online queries for learners' states with respect to various types of learning content. For example, the described techniques may serve a query to determine items that have been completed by a particular user, or courses with which a user has recently interacted. More particularly, the described techniques provide a uniform interface for all such learner progress, which facilitates fast and efficient capabilities of users, administrators, and developers with respect to finding, using, and modifying learner progress.

Abstract: Techniques described herein provide a service and associated interface that are capable of serving online queries for learners' states with respect to various types of learning content. For example, the described techniques may serve a query to determine items that have been completed by a particular user, or courses with which a user has recently interacted. More particularly, the described techniques provide a uniform interface for all such learner progress, which facilitates fast and efficient capabilities of users, administrators, and developers with respect to finding, using, and modifying learner progress.

Abstract: Techniques described herein provide a service and associated interface that are capable of serving online queries for learners' states with respect to various types of learning content. For example, the described techniques may serve a query to determine items that have been completed by a particular user, or courses with which a user has recently interacted. More particularly, the described techniques provide a uniform interface for all such learner progress, which facilitates fast and efficient capabilities of users, administrators, and developers with respect to finding, using, and modifying learner progress.