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Git diff-style approach for improve
function
#1005
Conversation
…o diff-syntax-for-improve-command � Conflicts: � gpt_engineer/preprompts/improve
…arting with add lines
@AntonOsika, @ATheorell, @captivus, @TheoMcCabe, @viborc I am reaching out to request your thorough review and feedback on PR #1005 for the gpt-engineer project. This pull request represents a significant advancement in our code improvement process, implementing a Git diff-style approach to enhance the Key Changes:
ExampleEnhanced Efficiency for Bulk ChangesThis PR introduces significant enhancements, allowing us to request improvements from the LLMS seamlessly. Previously, encountering the Visual Demonstrations:To provide clarity on these enhancements, here are some visual representations:
Request for Review:Your insights and expertise are invaluable in ensuring the effectiveness and quality of these enhancements. Please review and feedback on this PR to help identify any areas for improvement or optimization. Thank you for your time and attention to this matter. Best regards, |
Git diff-style approach for
improve
functionOverview
This pull request brings substantial enhancements to the
improve
function within the gpt-engineer project, incorporating a Git diff-style approach for parsing chats from LLMs. The modifications encompass the addition of a newpreprompt
file tailored for theimprove
function, the introduction of a new class named "Diff.py" designed to emulate Git diffs for validation and correction purposes, updates to thechat_to_files.py
andsteps.py
files located in the core directory, and an extensive set of test files and scenarios in the testing suite.Example of git diff
Motivation
Expectation
The integration of a Git diff-style parser significantly mitigates parsing issues. The introduction of enhanced validation and correction functionalities within the parsing segment ensures that common errors stemming from LLMs are rectified, allowing only legitimate modifications to be implemented in the user's files. The utilization of a succinct preprompt alongside robust validation mechanisms contributes to reduced token consumption and improved performance, particularly in the refinement of complex and extensive code files.