Adding support for o1-preview

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PR Type

enhancement, tests

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Description

• Added support for the o1-preview model in AICaller.py, including specific handling for non-streaming responses.
• Enhanced the streaming logic and error handling in the call_model method.
• Improved documentation in ReportGenerator.py to clarify the diff generation process.
• Updated UnitTestGenerator.py to set the model to o1-preview for test generation and ensure non-streaming calls.
• Added a new test in tests/test_ReportGenerator.py to verify the correctness of partial diff generation.
• Increased the maximum iterations for coverage testing in tests_integration/increase_coverage.py.

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Changes walkthrough 📝

	Relevant files
Enhancement	AICaller.py Add support for `o1-preview` and enhance streaming logic  cover_agent/AICaller.py Added support for o1-preview model with specific parameters. Introduced non-streaming response handling. Adjusted streaming logic and error handling. +45/-29  UnitTestGenerator.py Update model settings for test generation                                cover_agent/UnitTestGenerator.py Set model to o1-preview for test generation. Ensured non-streaming call to AI model. +3/-1
Documentation	ReportGenerator.py Improve documentation for diff generation method                  cover_agent/ReportGenerator.py Added detailed comments on diff generation. Clarified the use of difflib.unified_diff. +8/-2
Tests	test_ReportGenerator.py Add test for partial diff generation                                          tests/test_ReportGenerator.py Added a test for generate_partial_diff. Verified HTML diff output correctness. +10/-0
Configuration changes	increase_coverage.py Increase max iterations for coverage testing                          tests_integration/increase_coverage.py Increased maximum iterations for coverage. +1/-1

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PR Reviewer Guide 🔍

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

🧪 PR contains tests

🔒 No security concerns identified

⚡ Key issues to review Model-specific Logic The addition of model-specific logic for 'o1-preview' may lead to maintenance issues as more models are added. Consider implementing a more scalable approach, such as a configuration dictionary or separate handler functions for different models. Model Switching Temporarily changing the model to 'o1-preview' and then switching back might lead to unexpected behavior if exceptions occur. Consider using a context manager or try-finally block to ensure the model is always reset.

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PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Score
Error handling	Implement more robust error handling for streaming responses Consider using a more robust error handling mechanism for the streaming response. Instead of just printing the error, you could log it properly, retry the request, or handle specific types of exceptions differently. cover_agent/AICaller.py [76-83] -try: - for chunk in response: - print(chunk.choices[0].delta.content or "", end="", flush=True) - chunks.append(chunk) - time.sleep( - 0.01 - ) # Optional: Delay to simulate more 'natural' response pacing -except Exception as e: - print(f"Error during streaming: {e}") +import logging +from tenacity import retry, stop_after_attempt, wait_exponential +@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10)) +def stream_response(response): + chunks = [] + try: + for chunk in response: + print(chunk.choices[0].delta.content or "", end="", flush=True) + chunks.append(chunk) + time.sleep(0.01) # Optional: Delay to simulate more 'natural' response pacing + except Exception as e: + logging.error(f"Error during streaming: {e}") + raise + return chunks + +chunks = stream_response(response) + Apply this suggestion Suggestion importance[1-10]: 8 Why: The suggestion to enhance error handling by logging errors and potentially retrying requests provides significant improvements in robustness and reliability, especially in production environments.	8
Best practice	Use a context manager for temporary model changes Instead of directly modifying the self.ai_caller.model attribute, consider creating a context manager or a method in the AICaller class to temporarily change the model. This approach would be more robust and less prone to errors if an exception occurs during the API call. cover_agent/UnitTestGenerator.py [404-408] -self.ai_caller.model = "o1-preview" -response, prompt_token_count, response_token_count = ( - self.ai_caller.call_model(prompt=self.prompt, max_tokens=max_tokens, stream=False) -) -self.ai_caller.model = self.llm_model +class ModelContext: + def __init__(self, ai_caller, temp_model): + self.ai_caller = ai_caller + self.temp_model = temp_model + self.original_model = ai_caller.model + def __enter__(self): + self.ai_caller.model = self.temp_model + + def __exit__(self, exc_type, exc_val, exc_tb): + self.ai_caller.model = self.original_model + +with ModelContext(self.ai_caller, "o1-preview"): + response, prompt_token_count, response_token_count = ( + self.ai_caller.call_model(prompt=self.prompt, max_tokens=max_tokens, stream=False) + ) + Apply this suggestion Suggestion importance[1-10]: 8 Why: Using a context manager for temporary model changes is a best practice that ensures the model is reset even if an exception occurs, reducing the risk of unintended side effects.	8
Maintainability	Extract model-specific adjustments into a separate method Consider extracting the model-specific adjustments for 'o1-preview' into a separate method to improve code readability and maintainability. This will make it easier to add support for other models in the future and keep the call_model method cleaner. cover_agent/AICaller.py [55-60] -# Model-specific adjustments -if self.model == "o1-preview": +def adjust_params_for_o1_preview(self, completion_params, max_tokens): completion_params["temperature"] = 1 completion_params["stream"] = False # o1-preview doesn't support streaming completion_params["max_completion_tokens"] = max_tokens completion_params.pop("max_tokens", None) # Remove 'max_tokens' if present completion_params['messages'] = [msg for msg in completion_params['messages'] if msg['role'] != 'system'] # Popping off the system message +# Model-specific adjustments +if self.model == "o1-preview": + self.adjust_params_for_o1_preview(completion_params, max_tokens) + Apply this suggestion Suggestion importance[1-10]: 7 Why: Extracting model-specific adjustments into a separate method improves code readability and maintainability, making it easier to manage and extend support for additional models in the future.	7
Enhancement	Enhance test coverage for diff output completeness Enhance the test case for generate_partial_diff by adding assertions for the presence of diff header lines and context lines. This will ensure that the diff output includes all essential parts of a unified diff. tests/test_ReportGenerator.py [48-55] def test_generate_partial_diff_basic(self): original = "line1\nline2\nline3" processed = "line1\nline2 modified\nline3\nline4" diff_output = ReportGenerator.generate_partial_diff(original, processed) assert '<span class="diff-added">+line2 modified</span>' in diff_output assert '<span class="diff-added">+line4</span>' in diff_output assert '<span class="diff-removed">-line2</span>' in diff_output assert '<span class="diff-unchanged"> line1</span>' in diff_output + assert '<span class="diff-header">---' in diff_output + assert '<span class="diff-header">+++' in diff_output + assert '<span class="diff-hunk">@@' in diff_output Apply this suggestion Suggestion importance[1-10]: 6 Why: Adding assertions for diff headers and context lines in the test enhances coverage and ensures that all essential parts of the diff output are verified, improving test robustness.	6
Organization best practice	Implement thread-safe modifications to the completion_params dictionary Consider using a threading lock or other synchronization mechanism when modifying the completion_params dictionary to ensure thread-safety. This is especially important if the call_model method might be called concurrently from multiple threads. cover_agent/AICaller.py [46-61] -completion_params = { - "model": self.model, - "messages": messages, - "stream": stream, # Use the stream parameter passed to the method - "temperature": 0.2, - "max_tokens": max_tokens, -} +import threading -# Model-specific adjustments -if self.model == "o1-preview": - completion_params["temperature"] = 1 - completion_params["stream"] = False # o1-preview doesn't support streaming - completion_params["max_completion_tokens"] = max_tokens - completion_params.pop("max_tokens", None) # Remove 'max_tokens' if present - completion_params['messages'] = [msg for msg in completion_params['messages'] if msg['role'] != 'system'] # Popping off the system message +completion_params_lock = threading.Lock() +with completion_params_lock: + completion_params = { + "model": self.model, + "messages": messages, + "stream": stream, # Use the stream parameter passed to the method + "temperature": 0.2, + "max_tokens": max_tokens, + } + + # Model-specific adjustments + if self.model == "o1-preview": + completion_params["temperature"] = 1 + completion_params["stream"] = False # o1-preview doesn't support streaming + completion_params["max_completion_tokens"] = max_tokens + completion_params.pop("max_tokens", None) # Remove 'max_tokens' if present + completion_params['messages'] = [msg for msg in completion_params['messages'] if msg['role'] != 'system'] # Popping off the system message + Apply this suggestion Suggestion importance[1-10]: 5 Why: While the suggestion to use a threading lock for modifying the completion_params dictionary could enhance thread safety, it is only relevant if the method is expected to be called concurrently, which is not clear from the context. Thus, the impact is moderate.	5

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