mirror of
https://github.com/qodo-ai/pr-agent.git
synced 2025-07-03 20:30:41 +08:00
Merge branch 'main' into nocode_suggestions_config
This commit is contained in:
Tal
@ -1,6 +1,7 @@
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import asyncio
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import copy
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import textwrap
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import traceback
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from functools import partial
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from typing import Dict, List
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from jinja2 import Environment, StrictUndefined
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@ -44,7 +45,7 @@ class PRCodeSuggestions:
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self.is_extended = self._get_is_extended(args or [])
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except:
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self.is_extended = False
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num_code_suggestions = get_settings().pr_code_suggestions.num_code_suggestions_per_chunk
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num_code_suggestions = int(get_settings().pr_code_suggestions.num_code_suggestions_per_chunk)
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self.ai_handler = ai_handler()
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@ -69,6 +70,7 @@ class PRCodeSuggestions:
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"description": self.pr_description,
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"language": self.main_language,
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"diff": "", # empty diff for initial calculation
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"diff_no_line_numbers": "", # empty diff for initial calculation
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"num_code_suggestions": num_code_suggestions,
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"extra_instructions": get_settings().pr_code_suggestions.extra_instructions,
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"commit_messages_str": self.git_provider.get_commit_messages(),
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@ -110,18 +112,17 @@ class PRCodeSuggestions:
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if not data:
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data = {"code_suggestions": []}
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if (data is None or 'code_suggestions' not in data or not data['code_suggestions']
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and get_settings().config.publish_output):
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if (data is None or 'code_suggestions' not in data or not data['code_suggestions']):
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pr_body = "## PR Code Suggestions ✨\n\nNo code suggestions found for the PR."
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get_logger().warning('No code suggestions found for the PR.')
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if (get_settings().config.publish_output_no_suggestions):
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pr_body = "## PR Code Suggestions ✨\n\nNo code suggestions found for the PR."
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if get_settings().config.publish_output and get_settings().config.publish_output_no_suggestions:
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get_logger().debug(f"PR output", artifact=pr_body)
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if self.progress_response:
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self.git_provider.edit_comment(self.progress_response, body=pr_body)
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else:
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self.git_provider.publish_comment(pr_body)
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else:
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get_settings().data = {"artifact": ""}
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return
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if (not self.is_extended and get_settings().pr_code_suggestions.rank_suggestions) or \
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@ -198,8 +199,11 @@ class PRCodeSuggestions:
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self.git_provider.remove_comment(self.progress_response)
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else:
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get_logger().info('Code suggestions generated for PR, but not published since publish_output is False.')
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get_settings().data = {"artifact": data}
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return
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except Exception as e:
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get_logger().error(f"Failed to generate code suggestions for PR, error: {e}")
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get_logger().error(f"Failed to generate code suggestions for PR, error: {e}",
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artifact={"traceback": traceback.format_exc()})
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if get_settings().config.publish_output:
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if self.progress_response:
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self.progress_response.delete()
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@ -331,7 +335,7 @@ class PRCodeSuggestions:
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if self.patches_diff:
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get_logger().debug(f"PR diff", artifact=self.patches_diff)
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self.prediction = await self._get_prediction(model, self.patches_diff)
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self.prediction = await self._get_prediction(model, self.patches_diff, self.patches_diff_no_line_number)
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else:
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get_logger().warning(f"Empty PR diff")
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self.prediction = None
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@ -339,54 +343,76 @@ class PRCodeSuggestions:
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data = self.prediction
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return data
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async def _get_prediction(self, model: str, patches_diff: str) -> dict:
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async def _get_prediction(self, model: str, patches_diff: str, patches_diff_no_line_number: str) -> dict:
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variables = copy.deepcopy(self.vars)
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variables["diff"] = patches_diff # update diff
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variables["diff_no_line_numbers"] = patches_diff_no_line_number # update diff
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environment = Environment(undefined=StrictUndefined)
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system_prompt = environment.from_string(self.pr_code_suggestions_prompt_system).render(variables)
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user_prompt = environment.from_string(get_settings().pr_code_suggestions_prompt.user).render(variables)
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response, finish_reason = await self.ai_handler.chat_completion(
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model=model, temperature=get_settings().config.temperature, system=system_prompt, user=user_prompt)
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if not get_settings().config.publish_output:
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get_settings().system_prompt = system_prompt
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get_settings().user_prompt = user_prompt
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# load suggestions from the AI response
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data = self._prepare_pr_code_suggestions(response)
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# self-reflect on suggestions
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if get_settings().pr_code_suggestions.self_reflect_on_suggestions:
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model_turbo = get_settings().config.model_turbo # use turbo model for self-reflection, since it is an easier task
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response_reflect = await self.self_reflect_on_suggestions(data["code_suggestions"],
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patches_diff, model=model_turbo)
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if response_reflect:
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response_reflect_yaml = load_yaml(response_reflect)
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code_suggestions_feedback = response_reflect_yaml["code_suggestions"]
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if len(code_suggestions_feedback) == len(data["code_suggestions"]):
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for i, suggestion in enumerate(data["code_suggestions"]):
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try:
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suggestion["score"] = code_suggestions_feedback[i]["suggestion_score"]
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suggestion["score_why"] = code_suggestions_feedback[i]["why"]
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except Exception as e: #
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get_logger().error(f"Error processing suggestion score {i}",
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artifact={"suggestion": suggestion,
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"code_suggestions_feedback": code_suggestions_feedback[i]})
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suggestion["score"] = 7
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suggestion["score_why"] = ""
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# if the before and after code is the same, clear one of them
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try:
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if suggestion['existing_code'] == suggestion['improved_code']:
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get_logger().debug(
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f"edited improved suggestion {i + 1}, because equal to existing code: {suggestion['existing_code']}")
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if get_settings().pr_code_suggestions.commitable_code_suggestions:
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suggestion['improved_code'] = "" # we need 'existing_code' to locate the code in the PR
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else:
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suggestion['existing_code'] = ""
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except Exception as e:
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get_logger().error(f"Error processing suggestion {i + 1}, error: {e}")
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else:
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# get_logger().error(f"Could not self-reflect on suggestions. using default score 7")
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# self-reflect on suggestions (mandatory, since line numbers are generated now here)
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model_reflection = get_settings().config.model
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response_reflect = await self.self_reflect_on_suggestions(data["code_suggestions"],
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patches_diff, model=model_reflection)
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if response_reflect:
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response_reflect_yaml = load_yaml(response_reflect)
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code_suggestions_feedback = response_reflect_yaml["code_suggestions"]
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if len(code_suggestions_feedback) == len(data["code_suggestions"]):
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for i, suggestion in enumerate(data["code_suggestions"]):
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suggestion["score"] = 7
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suggestion["score_why"] = ""
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try:
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suggestion["score"] = code_suggestions_feedback[i]["suggestion_score"]
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suggestion["score_why"] = code_suggestions_feedback[i]["why"]
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if 'relevant_lines_start' not in suggestion:
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relevant_lines_start = code_suggestions_feedback[i].get('relevant_lines_start', -1)
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relevant_lines_end = code_suggestions_feedback[i].get('relevant_lines_end', -1)
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suggestion['relevant_lines_start'] = relevant_lines_start
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suggestion['relevant_lines_end'] = relevant_lines_end
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if relevant_lines_start < 0 or relevant_lines_end < 0:
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suggestion["score"] = 0
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try:
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if get_settings().config.publish_output:
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suggestion_statistics_dict = {'score': int(suggestion["score"]),
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'label': suggestion["label"].lower().strip()}
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get_logger().info(f"PR-Agent suggestions statistics",
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statistics=suggestion_statistics_dict, analytics=True)
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except Exception as e:
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get_logger().error(f"Failed to log suggestion statistics, error: {e}")
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pass
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except Exception as e: #
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get_logger().error(f"Error processing suggestion score {i}",
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artifact={"suggestion": suggestion,
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"code_suggestions_feedback": code_suggestions_feedback[i]})
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suggestion["score"] = 7
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suggestion["score_why"] = ""
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# if the before and after code is the same, clear one of them
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try:
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if suggestion['existing_code'] == suggestion['improved_code']:
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get_logger().debug(
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f"edited improved suggestion {i + 1}, because equal to existing code: {suggestion['existing_code']}")
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if get_settings().pr_code_suggestions.commitable_code_suggestions:
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suggestion['improved_code'] = "" # we need 'existing_code' to locate the code in the PR
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else:
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suggestion['existing_code'] = ""
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except Exception as e:
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get_logger().error(f"Error processing suggestion {i + 1}, error: {e}")
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else:
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# get_logger().error(f"Could not self-reflect on suggestions. using default score 7")
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for i, suggestion in enumerate(data["code_suggestions"]):
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suggestion["score"] = 7
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suggestion["score_why"] = ""
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return data
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@ -396,10 +422,10 @@ class PRCodeSuggestions:
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suggestion_truncation_message = get_settings().get("PR_CODE_SUGGESTIONS.SUGGESTION_TRUNCATION_MESSAGE", "")
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if max_code_suggestion_length > 0:
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if len(suggestion['improved_code']) > max_code_suggestion_length:
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suggestion['improved_code'] = suggestion['improved_code'][:max_code_suggestion_length]
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suggestion['improved_code'] += f"\n{suggestion_truncation_message}"
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get_logger().info(f"Truncated suggestion from {len(suggestion['improved_code'])} "
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f"characters to {max_code_suggestion_length} characters")
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suggestion['improved_code'] = suggestion['improved_code'][:max_code_suggestion_length]
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suggestion['improved_code'] += f"\n{suggestion_truncation_message}"
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return suggestion
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def _prepare_pr_code_suggestions(self, predictions: str) -> Dict:
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@ -414,8 +440,7 @@ class PRCodeSuggestions:
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one_sentence_summary_list = []
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for i, suggestion in enumerate(data['code_suggestions']):
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try:
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needed_keys = ['one_sentence_summary', 'label', 'relevant_file', 'relevant_lines_start',
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'relevant_lines_end']
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needed_keys = ['one_sentence_summary', 'label', 'relevant_file']
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is_valid_keys = True
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for key in needed_keys:
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if key not in suggestion:
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@ -539,9 +564,33 @@ class PRCodeSuggestions:
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return True
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return False
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def remove_line_numbers(self, patches_diff_list: List[str]) -> List[str]:
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# create a copy of the patches_diff_list, without line numbers for '__new hunk__' sections
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try:
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self.patches_diff_list_no_line_numbers = []
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for patches_diff in self.patches_diff_list:
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patches_diff_lines = patches_diff.splitlines()
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for i, line in enumerate(patches_diff_lines):
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if line.strip():
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if line[0].isdigit():
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# find the first letter in the line that starts with a valid letter
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for j, char in enumerate(line):
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if not char.isdigit():
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patches_diff_lines[i] = line[j + 1:]
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break
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self.patches_diff_list_no_line_numbers.append('\n'.join(patches_diff_lines))
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return self.patches_diff_list_no_line_numbers
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except Exception as e:
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get_logger().error(f"Error removing line numbers from patches_diff_list, error: {e}")
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return patches_diff_list
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async def _prepare_prediction_extended(self, model: str) -> dict:
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self.patches_diff_list = get_pr_multi_diffs(self.git_provider, self.token_handler, model,
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max_calls=get_settings().pr_code_suggestions.max_number_of_calls)
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# create a copy of the patches_diff_list, without line numbers for '__new hunk__' sections
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self.patches_diff_list_no_line_numbers = self.remove_line_numbers(self.patches_diff_list)
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if self.patches_diff_list:
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get_logger().info(f"Number of PR chunk calls: {len(self.patches_diff_list)}")
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get_logger().debug(f"PR diff:", artifact=self.patches_diff_list)
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@ -549,12 +598,14 @@ class PRCodeSuggestions:
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# parallelize calls to AI:
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if get_settings().pr_code_suggestions.parallel_calls:
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prediction_list = await asyncio.gather(
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*[self._get_prediction(model, patches_diff) for patches_diff in self.patches_diff_list])
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*[self._get_prediction(model, patches_diff, patches_diff_no_line_numbers) for
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patches_diff, patches_diff_no_line_numbers in
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zip(self.patches_diff_list, self.patches_diff_list_no_line_numbers)])
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self.prediction_list = prediction_list
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else:
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prediction_list = []
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for i, patches_diff in enumerate(self.patches_diff_list):
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prediction = await self._get_prediction(model, patches_diff)
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for patches_diff, patches_diff_no_line_numbers in zip(self.patches_diff_list, self.patches_diff_list_no_line_numbers):
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prediction = await self._get_prediction(model, patches_diff, patches_diff_no_line_numbers)
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prediction_list.append(prediction)
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data = {"code_suggestions": []}
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@ -563,18 +614,16 @@ class PRCodeSuggestions:
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score_threshold = max(1, int(get_settings().pr_code_suggestions.suggestions_score_threshold))
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for i, prediction in enumerate(predictions["code_suggestions"]):
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try:
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if get_settings().pr_code_suggestions.self_reflect_on_suggestions:
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score = int(prediction.get("score", 1))
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if score >= score_threshold:
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data["code_suggestions"].append(prediction)
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else:
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get_logger().info(
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f"Removing suggestions {i} from call {j}, because score is {score}, and score_threshold is {score_threshold}",
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artifact=prediction)
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else:
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score = int(prediction.get("score", 1))
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if score >= score_threshold:
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data["code_suggestions"].append(prediction)
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else:
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get_logger().info(
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f"Removing suggestions {i} from call {j}, because score is {score}, and score_threshold is {score_threshold}",
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artifact=prediction)
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except Exception as e:
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get_logger().error(f"Error getting PR diff for suggestion {i} in call {j}, error: {e}")
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get_logger().error(f"Error getting PR diff for suggestion {i} in call {j}, error: {e}",
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artifact={"prediction": prediction})
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self.data = data
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else:
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get_logger().warning(f"Empty PR diff list")
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@ -625,7 +674,7 @@ class PRCodeSuggestions:
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if get_settings().pr_code_suggestions.final_clip_factor != 1:
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max_len = max(
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len(data_sorted),
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get_settings().pr_code_suggestions.num_code_suggestions_per_chunk,
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int(get_settings().pr_code_suggestions.num_code_suggestions_per_chunk),
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)
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new_len = int(0.5 + max_len * get_settings().pr_code_suggestions.final_clip_factor)
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if new_len < len(data_sorted):
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@ -658,10 +707,7 @@ class PRCodeSuggestions:
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header = f"Suggestion"
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delta = 66
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header += " " * delta
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if get_settings().pr_code_suggestions.self_reflect_on_suggestions:
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pr_body += f"""<thead><tr><td>Category</td><td align=left>{header}</td><td align=center>Score</td></tr>"""
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else:
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pr_body += f"""<thead><tr><td>Category</td><td align=left>{header}</td></tr>"""
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pr_body += f"""<thead><tr><td>Category</td><td align=left>{header}</td><td align=center>Score</td></tr>"""
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pr_body += """<tbody>"""
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suggestions_labels = dict()
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# add all suggestions related to each label
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@ -672,12 +718,11 @@ class PRCodeSuggestions:
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suggestions_labels[label].append(suggestion)
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# sort suggestions_labels by the suggestion with the highest score
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if get_settings().pr_code_suggestions.self_reflect_on_suggestions:
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suggestions_labels = dict(
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sorted(suggestions_labels.items(), key=lambda x: max([s['score'] for s in x[1]]), reverse=True))
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# sort the suggestions inside each label group by score
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for label, suggestions in suggestions_labels.items():
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suggestions_labels[label] = sorted(suggestions, key=lambda x: x['score'], reverse=True)
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suggestions_labels = dict(
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sorted(suggestions_labels.items(), key=lambda x: max([s['score'] for s in x[1]]), reverse=True))
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# sort the suggestions inside each label group by score
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for label, suggestions in suggestions_labels.items():
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suggestions_labels[label] = sorted(suggestions, key=lambda x: x['score'], reverse=True)
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counter_suggestions = 0
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for label, suggestions in suggestions_labels.items():
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@ -736,16 +781,14 @@ class PRCodeSuggestions:
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{example_code.rstrip()}
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"""
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if get_settings().pr_code_suggestions.self_reflect_on_suggestions:
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pr_body += f"<details><summary>Suggestion importance[1-10]: {suggestion['score']}</summary>\n\n"
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pr_body += f"Why: {suggestion['score_why']}\n\n"
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pr_body += f"</details>"
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pr_body += f"<details><summary>Suggestion importance[1-10]: {suggestion['score']}</summary>\n\n"
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pr_body += f"Why: {suggestion['score_why']}\n\n"
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pr_body += f"</details>"
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pr_body += f"</details>"
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# # add another column for 'score'
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if get_settings().pr_code_suggestions.self_reflect_on_suggestions:
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pr_body += f"</td><td align=center>{suggestion['score']}\n\n"
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pr_body += f"</td><td align=center>{suggestion['score']}\n\n"
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pr_body += f"</td></tr>"
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counter_suggestions += 1
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