Sandboxed LLM Code Interpreter for Reliable Task Automation
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Solution Overview
Problem
Foundation models (FMs) require a supervised environment for code execution, necessitating user intervention, limiting their accessibility and usability for non-engineers.
Innovation Solution
A controlled Large Language Model (LLM) Code Interpreter and Execution Environment (LCIEE) that generates and executes code safely, managing errors and accessing external resources, allowing users to automate tasks with natural language inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a controlled execution environment is implemented for LLM-generated code, then safety and reliability are improved, but device complexity increases
Solution Approach 1:
The patent introduces a controlled execution environment that acts as an intermediary layer between the LLM code generator and the actual code execution. This environment includes a sandboxed runtime, package manager, and process controller that mediate all interactions, ensuring safety and reliability while managing the complexity of executing untrusted generated code.
Solution Approach 2:
The execution environment is segmented into distinct functional components: code generation module, controlled execution environment (with sandboxing), package management system, and error handling layer. This segmentation isolates different functions, allowing each to be optimized and managed independently while contributing to overall system reliability.
2Productivity
If automated code execution is implemented without supervision, then productivity increases, but reliability deteriorates
Solution Approach 1:
The controlled execution environment provides self-service capabilities through automated package management (resolving dependencies and installing required packages), automatic error detection and reporting, and self-contained sandboxing that doesn't require external supervision for basic safety operations. This maintains productivity while ensuring reliability through automated safeguards.
Solution Approach 2:
The system implements comprehensive feedback mechanisms including real-time monitoring of code execution, automatic error detection and reporting back to users, and performance metrics collection. This feedback loop enables the system to maintain reliability by detecting and reporting issues automatically while preserving productivity through continuous operation.
3Reliability
If error detection and correction mechanisms are added to the execution environment, then reliability is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-configuring the sandboxed execution environment with necessary security constraints, package management rules, and error handling protocols before code execution begins. Dependencies are resolved and packages are prepared in advance, reducing the complexity of error management during actual execution while maintaining high reliability.
4Ease of operation
If package management and dependency resolution are automated, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The package management system operates autonomously within the controlled environment, automatically resolving dependencies, downloading required packages, and configuring the execution context without user intervention. This self-service capability significantly improves ease of operation while the automated nature of the process manages the inherent complexity internally.
Data Source
AI summary
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.