mirror of
https://github.com/qodo-ai/pr-agent.git
synced 2025-07-02 03:40:38 +08:00
improve --extend
This commit is contained in:
mrT23
@ -11,7 +11,6 @@ from pr_agent.tools.pr_description import PRDescription
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from pr_agent.tools.pr_information_from_user import PRInformationFromUser
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from pr_agent.tools.pr_questions import PRQuestions
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from pr_agent.tools.pr_reviewer import PRReviewer
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from pr_agent.tools.pr_extended_code_suggestions import PRExtendedCodeSuggestions
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from pr_agent.tools.pr_update_changelog import PRUpdateChangelog
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from pr_agent.tools.pr_config import PRConfig
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@ -26,7 +25,6 @@ command2class = {
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"describe_pr": PRDescription,
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"improve": PRCodeSuggestions,
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"improve_code": PRCodeSuggestions,
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"extended_improve": PRExtendedCodeSuggestions,
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"ask": PRQuestions,
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"ask_question": PRQuestions,
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"update_changelog": PRUpdateChangelog,
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@ -247,7 +247,8 @@ def update_settings_from_args(args: List[str]) -> List[str]:
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arg = arg.strip('-').strip()
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vals = arg.split('=', 1)
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if len(vals) != 2:
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logging.error(f'Invalid argument format: {arg}')
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if len(vals) > 2: # --extended is a valid argument
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logging.error(f'Invalid argument format: {arg}')
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other_args.append(arg)
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continue
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key, value = _fix_key_value(*vals)
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@ -19,13 +19,21 @@ For example:
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- cli.py --pr_url=... reflect
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Supported commands:
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review / review_pr - Add a review that includes a summary of the PR and specific suggestions for improvement.
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ask / ask_question [question] - Ask a question about the PR.
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describe / describe_pr - Modify the PR title and description based on the PR's contents.
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improve / improve_code - Suggest improvements to the code in the PR as pull request comments ready to commit.
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reflect - Ask the PR author questions about the PR.
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update_changelog - Update the changelog based on the PR's contents.
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-review / review_pr - Add a review that includes a summary of the PR and specific suggestions for improvement.
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-ask / ask_question [question] - Ask a question about the PR.
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-describe / describe_pr - Modify the PR title and description based on the PR's contents.
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-improve / improve_code - Suggest improvements to the code in the PR as pull request comments ready to commit.
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Extended mode ('improve --extended') employs several calls, and provides a more thorough feedback
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-reflect - Ask the PR author questions about the PR.
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-update_changelog - Update the changelog based on the PR's contents.
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Configuration:
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To edit any configuration parameter from 'configuration.toml', just add -config_path=<value>.
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For example: 'python cli.py --pr_url=... review --pr_reviewer.extra_instructions="focus on the file: ..."'
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""")
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@ -1,7 +1,7 @@
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commands_text = "> **/review [-i]**: Request a review of your Pull Request. For an incremental review, which only " \
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"considers changes since the last review, include the '-i' option.\n" \
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"> **/describe**: Modify the PR title and description based on the contents of the PR.\n" \
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"> **/improve**: Suggest improvements to the code in the PR. \n" \
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"> **/improve [--extended]**: Suggest improvements to the code in the PR. Extended mode employs several calls, and provides a more thorough feedback. \n" \
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"> **/ask \\<QUESTION\\>**: Pose a question about the PR.\n" \
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"> **/update_changelog**: Update the changelog based on the PR's contents.\n\n" \
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">To edit any configuration parameter from **configuration.toml**, add --config_path=new_value\n" \
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@ -31,14 +31,12 @@ extra_instructions = ""
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[pr_code_suggestions] # /improve #
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num_code_suggestions=4
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extra_instructions = ""
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[pr_extendeted_code_suggestions] # /extended_improve #
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rank_suggestions = false
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# params for '/improve --extended' mode
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num_code_suggestions_per_chunk=8
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extra_instructions = ""
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rank_extended_suggestions = true
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max_number_of_calls = 5
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rank_suggestions = true
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final_clip_factor = 0.5
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final_clip_factor = 0.9
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[pr_update_changelog] # /update_changelog #
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push_changelog_changes=false
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@ -2,11 +2,13 @@ import copy
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import json
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import logging
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import textwrap
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from typing import List
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import yaml
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from jinja2 import Environment, StrictUndefined
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from pr_agent.algo.ai_handler import AiHandler
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from pr_agent.algo.pr_processing import get_pr_diff, retry_with_fallback_models
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from pr_agent.algo.pr_processing import get_pr_diff, retry_with_fallback_models, get_pr_multi_diffs
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from pr_agent.algo.token_handler import TokenHandler
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from pr_agent.algo.utils import try_fix_json
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from pr_agent.config_loader import get_settings
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@ -22,6 +24,13 @@ class PRCodeSuggestions:
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self.git_provider.get_languages(), self.git_provider.get_files()
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)
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# extended mode
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self.is_extended = any(["extended" in arg for arg in args])
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if self.is_extended:
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num_code_suggestions = get_settings().pr_code_suggestions.num_code_suggestions_per_chunk
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else:
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num_code_suggestions = get_settings().pr_code_suggestions.num_code_suggestions
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self.ai_handler = AiHandler()
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self.patches_diff = None
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self.prediction = None
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@ -32,7 +41,7 @@ class PRCodeSuggestions:
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"description": self.git_provider.get_pr_description(),
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"language": self.main_language,
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"diff": "", # empty diff for initial calculation
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"num_code_suggestions": get_settings().pr_code_suggestions.num_code_suggestions,
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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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}
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@ -42,14 +51,22 @@ class PRCodeSuggestions:
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get_settings().pr_code_suggestions_prompt.user)
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async def run(self):
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# assert type(self.git_provider) != BitbucketProvider, "Bitbucket is not supported for now"
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logging.info('Generating code suggestions for PR...')
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if get_settings().config.publish_output:
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self.git_provider.publish_comment("Preparing review...", is_temporary=True)
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await retry_with_fallback_models(self._prepare_prediction)
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logging.info('Preparing PR review...')
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data = self._prepare_pr_code_suggestions()
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if not self.is_extended:
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await retry_with_fallback_models(self._prepare_prediction)
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data = self._prepare_pr_code_suggestions()
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else:
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data = await retry_with_fallback_models(self._prepare_prediction_extended)
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if (not self.is_extended and get_settings().pr_code_suggestions.rank_suggestions) or \
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(self.is_extended and get_settings().pr_code_suggestions.rank_extended_suggestions):
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logging.info('Ranking Suggestions...')
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data['Code suggestions'] = await self.rank_suggestions(data['Code suggestions'])
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if get_settings().config.publish_output:
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logging.info('Pushing PR review...')
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self.git_provider.remove_initial_comment()
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@ -145,3 +162,81 @@ class PRCodeSuggestions:
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return new_code_snippet
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async def _prepare_prediction_extended(self, model: str) -> dict:
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logging.info('Getting PR diff...')
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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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logging.info('Getting multi AI predictions...')
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prediction_list = []
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for i, patches_diff in enumerate(patches_diff_list):
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logging.info(f"Processing chunk {i + 1} of {len(patches_diff_list)}")
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self.patches_diff = patches_diff
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prediction = await self._get_prediction(model)
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prediction_list.append(prediction)
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self.prediction_list = prediction_list
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data = {}
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for prediction in prediction_list:
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self.prediction = prediction
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data_per_chunk = self._prepare_pr_code_suggestions()
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if "Code suggestions" in data:
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data["Code suggestions"].extend(data_per_chunk["Code suggestions"])
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else:
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data.update(data_per_chunk)
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self.data = data
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return data
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async def rank_suggestions(self, data: List) -> List:
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"""
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Call a model to rank (sort) code suggestions based on their importance order.
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Args:
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data (List): A list of code suggestions to be ranked.
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Returns:
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List: The ranked list of code suggestions.
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"""
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suggestion_list = []
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# remove invalid suggestions
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for i, suggestion in enumerate(data):
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if suggestion['existing code'] != suggestion['improved code']:
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suggestion_list.append(suggestion)
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data_sorted = [[]] * len(suggestion_list)
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try:
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suggestion_str = ""
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for i, suggestion in enumerate(suggestion_list):
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suggestion_str += f"suggestion {i + 1}: " + str(suggestion) + '\n\n'
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variables = {'suggestion_list': suggestion_list, 'suggestion_str': suggestion_str}
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model = get_settings().config.model
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environment = Environment(undefined=StrictUndefined)
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system_prompt = environment.from_string(get_settings().pr_sort_code_suggestions_prompt.system).render(
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variables)
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user_prompt = environment.from_string(get_settings().pr_sort_code_suggestions_prompt.user).render(variables)
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if get_settings().config.verbosity_level >= 2:
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logging.info(f"\nSystem prompt:\n{system_prompt}")
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logging.info(f"\nUser prompt:\n{user_prompt}")
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response, finish_reason = await self.ai_handler.chat_completion(model=model, system=system_prompt,
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user=user_prompt)
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sort_order = yaml.safe_load(response)
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for s in sort_order['Sort Order']:
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suggestion_number = s['suggestion number']
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importance_order = s['importance order']
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data_sorted[importance_order - 1] = suggestion_list[suggestion_number - 1]
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if get_settings().pr_extendeted_code_suggestions.final_clip_factor != 1:
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new_len = int(0.5 + len(data_sorted) * get_settings().pr_extendeted_code_suggestions.final_clip_factor)
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data_sorted = data_sorted[:new_len]
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except Exception as e:
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if get_settings().config.verbosity_level >= 1:
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logging.info(f"Could not sort suggestions, error: {e}")
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data_sorted = suggestion_list
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return data_sorted
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@ -1,215 +0,0 @@
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from typing import List
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import copy
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import json
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import logging
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import textwrap
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from typing import Dict, Any
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import yaml
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from jinja2 import Environment, StrictUndefined
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from pr_agent.algo.ai_handler import AiHandler
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from pr_agent.algo.pr_processing import get_pr_multi_diffs, retry_with_fallback_models
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from pr_agent.algo.token_handler import TokenHandler
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from pr_agent.algo.utils import try_fix_json
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from pr_agent.config_loader import get_settings
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from pr_agent.git_providers import get_git_provider
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from pr_agent.git_providers.git_provider import get_main_pr_language
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class PRExtendedCodeSuggestions:
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def __init__(self, pr_url: str, cli_mode=False, args: list = None):
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self.git_provider = get_git_provider()(pr_url)
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self.main_language = get_main_pr_language(
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self.git_provider.get_languages(), self.git_provider.get_files()
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)
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self.ai_handler = AiHandler()
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self.patches_diff = None
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self.prediction = None
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self.cli_mode = cli_mode
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self.vars = {
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"title": self.git_provider.pr.title,
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"branch": self.git_provider.get_pr_branch(),
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"description": self.git_provider.get_pr_description(),
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"language": self.main_language,
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"diff": "", # empty diff for initial calculation
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"num_code_suggestions": get_settings().pr_extendeted_code_suggestions.num_code_suggestions_per_chunk,
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"extra_instructions": get_settings().pr_extendeted_code_suggestions.extra_instructions,
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"commit_messages_str": self.git_provider.get_commit_messages(),
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}
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self.token_handler = TokenHandler(self.git_provider.pr,
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self.vars,
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get_settings().pr_code_suggestions_prompt.system,
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get_settings().pr_code_suggestions_prompt.user)
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async def run(self):
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logging.info('Generating code suggestions for PR...')
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if get_settings().config.publish_output:
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self.git_provider.publish_comment("Preparing review...", is_temporary=True)
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data = await retry_with_fallback_models(self._prepare_prediction)
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if get_settings().pr_extendeted_code_suggestions.rank_suggestions:
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logging.info('Ranking Suggestions...')
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data['Code suggestions'] = await self.rank_suggestions(data['Code suggestions'])
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logging.info('Preparing PR review...')
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if get_settings().config.publish_output:
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logging.info('Pushing PR review...')
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self.git_provider.remove_initial_comment()
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logging.info('Pushing inline code comments...')
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self.push_inline_code_suggestions(data)
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async def _prepare_prediction(self, model: str) -> dict:
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logging.info('Getting PR diff...')
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patches_diff_list = get_pr_multi_diffs(self.git_provider, self.token_handler, model,
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max_calls=get_settings().pr_extendeted_code_suggestions.max_number_of_calls)
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logging.info('Getting multi AI predictions...')
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prediction_list = []
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for i, patches_diff in enumerate(patches_diff_list):
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logging.info(f"Processing chunk {i + 1} of {len(patches_diff_list)}")
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self.patches_diff = patches_diff
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prediction = await self._get_prediction(model)
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prediction_list.append(prediction)
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self.prediction_list = prediction_list
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data = {}
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for prediction in prediction_list:
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self.prediction = prediction
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data_per_chunk = self._prepare_pr_code_suggestions()
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if "Code suggestions" in data:
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data["Code suggestions"].extend(data_per_chunk["Code suggestions"])
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else:
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data.update(data_per_chunk)
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self.data = data
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return data
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async def _get_prediction(self, model: str):
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variables = copy.deepcopy(self.vars)
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variables["diff"] = self.patches_diff # update diff
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environment = Environment(undefined=StrictUndefined)
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system_prompt = environment.from_string(get_settings().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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if get_settings().config.verbosity_level >= 2:
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logging.info(f"\nSystem prompt:\n{system_prompt}")
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logging.info(f"\nUser prompt:\n{user_prompt}")
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response, finish_reason = await self.ai_handler.chat_completion(model=model, temperature=0.2,
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system=system_prompt, user=user_prompt)
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return response
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def _prepare_pr_code_suggestions(self) -> str:
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review = self.prediction.strip()
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try:
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data = json.loads(review)
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except json.decoder.JSONDecodeError:
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if get_settings().config.verbosity_level >= 1:
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logging.info(f"Could not parse json response: {review}")
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data = try_fix_json(review, code_suggestions=True)
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return data
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def push_inline_code_suggestions(self, data):
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code_suggestions = []
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if not data['Code suggestions']:
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return self.git_provider.publish_comment('No suggestions found to improve this PR.')
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for d in data['Code suggestions']:
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try:
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if get_settings().config.verbosity_level >= 1:
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logging.info(f"suggestion: {d}")
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relevant_file = d['relevant file'].strip()
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relevant_lines_str = d['relevant lines'].strip()
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if ',' in relevant_lines_str: # handling 'relevant lines': '181, 190' or '178-184, 188-194'
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relevant_lines_str = relevant_lines_str.split(',')[0]
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relevant_lines_start = int(relevant_lines_str.split('-')[0]) # absolute position
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relevant_lines_end = int(relevant_lines_str.split('-')[-1])
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content = d['suggestion content']
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new_code_snippet = d['improved code']
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if new_code_snippet:
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new_code_snippet = self.dedent_code(relevant_file, relevant_lines_start, new_code_snippet)
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body = f"**Suggestion:** {content}\n```suggestion\n" + new_code_snippet + "\n```"
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code_suggestions.append({'body': body, 'relevant_file': relevant_file,
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'relevant_lines_start': relevant_lines_start,
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'relevant_lines_end': relevant_lines_end})
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except Exception:
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if get_settings().config.verbosity_level >= 1:
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logging.info(f"Could not parse suggestion: {d}")
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self.git_provider.publish_code_suggestions(code_suggestions)
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def dedent_code(self, relevant_file, relevant_lines_start, new_code_snippet):
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try: # dedent code snippet
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self.diff_files = self.git_provider.diff_files if self.git_provider.diff_files \
|
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else self.git_provider.get_diff_files()
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original_initial_line = None
|
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for file in self.diff_files:
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if file.filename.strip() == relevant_file:
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original_initial_line = file.head_file.splitlines()[relevant_lines_start - 1]
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break
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if original_initial_line:
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suggested_initial_line = new_code_snippet.splitlines()[0]
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original_initial_spaces = len(original_initial_line) - len(original_initial_line.lstrip())
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suggested_initial_spaces = len(suggested_initial_line) - len(suggested_initial_line.lstrip())
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delta_spaces = original_initial_spaces - suggested_initial_spaces
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if delta_spaces > 0:
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new_code_snippet = textwrap.indent(new_code_snippet, delta_spaces * " ").rstrip('\n')
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||||
except Exception as e:
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||||
if get_settings().config.verbosity_level >= 1:
|
||||
logging.info(f"Could not dedent code snippet for file {relevant_file}, error: {e}")
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||||
|
||||
return new_code_snippet
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||||
|
||||
async def rank_suggestions(self, data: List) -> List:
|
||||
"""
|
||||
Call a model to rank (sort) code suggestions based on their importance order.
|
||||
|
||||
Args:
|
||||
data (List): A list of code suggestions to be ranked.
|
||||
|
||||
Returns:
|
||||
List: The ranked list of code suggestions.
|
||||
"""
|
||||
|
||||
suggestion_list = []
|
||||
# remove invalid suggestions
|
||||
for i, suggestion in enumerate(data):
|
||||
if suggestion['existing code'] != suggestion['improved code']:
|
||||
suggestion_list.append(suggestion)
|
||||
|
||||
data_sorted = [[]] * len(suggestion_list)
|
||||
|
||||
try:
|
||||
suggestion_str = ""
|
||||
for i, suggestion in enumerate(suggestion_list):
|
||||
suggestion_str += f"suggestion {i + 1}: " + str(suggestion) + '\n\n'
|
||||
|
||||
variables = {'suggestion_list': suggestion_list, 'suggestion_str': suggestion_str}
|
||||
model = get_settings().config.model
|
||||
environment = Environment(undefined=StrictUndefined)
|
||||
system_prompt = environment.from_string(get_settings().pr_sort_code_suggestions_prompt.system).render(variables)
|
||||
user_prompt = environment.from_string(get_settings().pr_sort_code_suggestions_prompt.user).render(variables)
|
||||
if get_settings().config.verbosity_level >= 2:
|
||||
logging.info(f"\nSystem prompt:\n{system_prompt}")
|
||||
logging.info(f"\nUser prompt:\n{user_prompt}")
|
||||
response, finish_reason = await self.ai_handler.chat_completion(model=model, system=system_prompt, user=user_prompt)
|
||||
|
||||
sort_order = yaml.safe_load(response)
|
||||
for s in sort_order['Sort Order']:
|
||||
suggestion_number = s['suggestion number']
|
||||
importance_order = s['importance order']
|
||||
data_sorted[importance_order - 1] = suggestion_list[suggestion_number - 1]
|
||||
|
||||
if get_settings().pr_extendeted_code_suggestions.final_clip_factor != 1:
|
||||
new_len = int(0.5 + len(data_sorted) * get_settings().pr_extendeted_code_suggestions.final_clip_factor)
|
||||
data_sorted = data_sorted[:new_len]
|
||||
except Exception as e:
|
||||
if get_settings().config.verbosity_level >= 1:
|
||||
logging.info(f"Could not sort suggestions, error: {e}")
|
||||
data_sorted = suggestion_list
|
||||
|
||||
return data_sorted
|
||||
Reference in New Issue
Block a user