[MRG] Github Integration - Information collector

[huangyz0918]

PR Type

enhancement, tests

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Description

• Implemented the GithubInte class to integrate with GitHub, allowing processing of source code, commit history, and issues/PRs.
• Added a test script in tests/main.py to demonstrate the functionality of the GithubInte class.
• Updated requirements.txt to include the github package as a dependency.

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Changes walkthrough 📝

	Relevant files
Enhancement	__init__.py Add import statement for Github integration                            mle/integration/init.py Added import statement for GithubInte class. +1/-0      github.py Implement Github integration class with repository processing mle/integration/github.py Implemented GithubInte class for GitHub integration. Added methods to process source code, commit history, and issues/PRs. Utilized GitHub API for repository interactions. +65/-0
Tests	main.py Add test script for Github integration functionality          tests/main.py Added test script to demonstrate GithubInte class usage. Printed outputs for source code, commit history, and issues/PRs. +9/-0
Dependencies	requirements.txt Update dependencies to include GitHub package                        requirements.txt Added github package to dependencies. +1/-0

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[github-actions]

PR Reviewer Guide 🔍

⏱️ Estimated effort to review: 3 🔵🔵🔵⚪⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Key issues to review Error Handling The method process_source_code uses a generic exception handling that catches all exceptions and prints an error message. This could be improved by handling specific exceptions or by re-raising exceptions after logging to not suppress unexpected issues. Security Concern The initialization of GithubInte class does not handle the scenario where both github_token and the environment variable GITHUB_TOKEN are missing. This could lead to unauthenticated requests which might be rate-limited or expose sensitive information.

[github-actions]

PR Code Suggestions ✨

Category	Suggestion	Score
Possible bug	Add error handling for content decoding to enhance application stability Handle potential exceptions that may arise from the base64.b64decode method to prevent the application from crashing. This can be done by wrapping the decoding logic in a try-except block. mle/integration/github.py [30] -file_text = base64.b64decode(file_content.content).decode('utf-8') +try: + file_text = base64.b64decode(file_content.content).decode('utf-8') +except Exception as e: + file_text = "Error decoding content" + print(f"Failed to decode {file_content.path}: {str(e)}") Suggestion importance[1-10]: 9 Why: Adding a try-except block around the base64 decoding process is crucial for preventing application crashes due to unexpected decoding errors, significantly improving the application's stability.	9
Best practice	Improve the robustness and security of environment variable retrieval Replace the direct use of os.getenv with a more secure and robust method to handle the absence of an environment variable. This can be achieved by using os.environ.get which allows for a default value if the environment variable is not set, thus avoiding potential runtime errors. mle/integration/github.py [16] -github_token = os.getenv("GITHUB_TOKEN") +github_token = os.environ.get("GITHUB_TOKEN", "your_default_token_here") Suggestion importance[1-10]: 8 Why: Using os.environ.get with a default value improves robustness by preventing potential runtime errors if the environment variable is not set, enhancing the security and reliability of the code.	8
Maintainability	Refactor code to improve maintainability by separating file content processing into a helper function Refactor the method process_source_code to separate concerns by creating a helper function that handles the retrieval and processing of file contents. This will make the code cleaner and more maintainable. mle/integration/github.py [21-37] while contents: file_content = contents.pop(0) + self.process_file_content(file_content, source_code) + +def process_file_content(self, file_content, source_code): if file_content.type == "dir": contents.extend(self.repo.get_contents(file_content.path)) else: try: if file_content.encoding == 'base64': file_text = base64.b64decode(file_content.content).decode('utf-8') else: file_text = file_content.decoded_content.decode('utf-8') source_code[file_content.path] = file_text except Exception as e: print(f"Error processing file {file_content.path}: {str(e)}") source_code[file_content.path] = "Unable to process content" Suggestion importance[1-10]: 7 Why: The refactoring suggestion improves code maintainability by separating concerns, making the code cleaner and easier to manage, although it does not address a critical issue.	7
Enhancement	Refactor to use list comprehensions for more efficient and readable code Use list comprehensions for more concise and Pythonic code when processing issues and pull requests in the process_issues_and_prs method. mle/integration/github.py [54-63] -for item in list(issues)[:limit] + list(prs)[:limit]: - item_type = 'Issue' if hasattr(item, 'issue') else 'PR' - issues_prs[f"{item_type}-{item.number}"] = { - "type": item_type, +issues_prs = { + f"{'Issue' if hasattr(item, 'issue') else 'PR'}-{item.number}": { + "type": 'Issue' if hasattr(item, 'issue') else 'PR', "number": item.number, "title": item.title, "state": item.state, "created": item.created_at.isoformat(), "author": item.user.login, "body": item.body[:200] + "..." if item.body else "" - } + } for item in list(issues)[:limit] + list(prs)[:limit] +} Suggestion importance[1-10]: 6 Why: Using list comprehensions can make the code more concise and readable, but the improvement is minor and does not significantly impact functionality or performance.	6

[huangyz0918]

The Github toolkit is ready for integrating with the RAG -- I will file another PR for the RAG

[huangyz0918]

An example summary for the previous week, fetched by the GitHub integration.

[huangyz0918]

A good way to build agents:

1. try to build the completed workflow without using AI and try our best to aggregate information
2. and then based on the aggregated information, introduce LLM into the existing workflow gradually
3. build functions for the agents to call actively