mirror of
https://github.com/qodo-ai/pr-agent.git
synced 2025-07-03 04:10:49 +08:00
194 lines
7.7 KiB
Python
194 lines
7.7 KiB
Python
import copy
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import json
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import logging
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from typing import List, Tuple
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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.token_handler import TokenHandler
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from pr_agent.algo.utils import load_yaml
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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 PRDescription:
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def __init__(self, pr_url: str, args: list = None):
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"""
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Initialize the PRDescription object with the necessary attributes and objects for generating a PR description
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using an AI model.
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Args:
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pr_url (str): The URL of the pull request.
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args (list, optional): List of arguments passed to the PRDescription class. Defaults to None.
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"""
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# Initialize the git provider and main PR language
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self.git_provider = get_git_provider()(pr_url)
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self.main_pr_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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# Initialize the AI handler
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self.ai_handler = AiHandler()
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# Initialize the variables dictionary
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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_pr_language,
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"diff": "", # empty diff for initial calculation
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"extra_instructions": get_settings().pr_description.extra_instructions,
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"commit_messages_str": self.git_provider.get_commit_messages()
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}
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self.user_description = self.git_provider.get_user_description()
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# Initialize the token handler
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self.token_handler = TokenHandler(
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self.git_provider.pr,
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self.vars,
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get_settings().pr_description_prompt.system,
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get_settings().pr_description_prompt.user,
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)
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# Initialize patches_diff and prediction attributes
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self.patches_diff = None
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self.prediction = None
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async def run(self):
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"""
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Generates a PR description using an AI model and publishes it to the PR.
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"""
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logging.info('Generating a PR description...')
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if get_settings().config.publish_output:
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self.git_provider.publish_comment("Preparing pr description...", is_temporary=True)
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await retry_with_fallback_models(self._prepare_prediction)
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logging.info('Preparing answer...')
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pr_title, pr_body, pr_types, markdown_text = self._prepare_pr_answer()
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if get_settings().config.publish_output:
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logging.info('Pushing answer...')
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if get_settings().pr_description.publish_description_as_comment:
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self.git_provider.publish_comment(markdown_text)
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else:
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self.git_provider.publish_description(pr_title, pr_body)
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if self.git_provider.is_supported("get_labels"):
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current_labels = self.git_provider.get_labels()
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if current_labels is None:
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current_labels = []
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self.git_provider.publish_labels(pr_types + current_labels)
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self.git_provider.remove_initial_comment()
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return ""
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async def _prepare_prediction(self, model: str) -> None:
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"""
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Prepare the AI prediction for the PR description based on the provided model.
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Args:
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model (str): The name of the model to be used for generating the prediction.
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Returns:
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None
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Raises:
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Any exceptions raised by the 'get_pr_diff' and '_get_prediction' functions.
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"""
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logging.info('Getting PR diff...')
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self.patches_diff = get_pr_diff(self.git_provider, self.token_handler, model)
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logging.info('Getting AI prediction...')
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self.prediction = await self._get_prediction(model)
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async def _get_prediction(self, model: str) -> str:
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"""
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Generate an AI prediction for the PR description based on the provided model.
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Args:
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model (str): The name of the model to be used for generating the prediction.
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Returns:
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str: The generated AI prediction.
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"""
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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_description_prompt.system).render(variables)
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user_prompt = environment.from_string(get_settings().pr_description_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(
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model=model,
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temperature=0.2,
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system=system_prompt,
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user=user_prompt
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)
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return response
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def _prepare_pr_answer(self) -> Tuple[str, str, List[str], str]:
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"""
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Prepare the PR description based on the AI prediction data.
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Returns:
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- title: a string containing the PR title.
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- pr_body: a string containing the PR body in a markdown format.
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- pr_types: a list of strings containing the PR types.
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- markdown_text: a string containing the AI prediction data in a markdown format. used for publishing a comment
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"""
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# Load the AI prediction data into a dictionary
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data = load_yaml(self.prediction.strip())
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if get_settings().pr_description.keep_user_description and self.user_description:
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data["User Description"] = self.user_description
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# Initialization
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pr_types = []
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# Iterate over the dictionary items and append the key and value to 'markdown_text' in a markdown format
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markdown_text = ""
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for key, value in data.items():
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markdown_text += f"## {key}\n\n"
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markdown_text += f"{value}\n\n"
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# If the 'PR Type' key is present in the dictionary, split its value by comma and assign it to 'pr_types'
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if 'PR Type' in data:
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if type(data['PR Type']) == list:
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pr_types = data['PR Type']
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elif type(data['PR Type']) == str:
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pr_types = data['PR Type'].split(',')
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# Assign the value of the 'PR Title' key to 'title' variable and remove it from the dictionary
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title = data.pop('PR Title')
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# Iterate over the remaining dictionary items and append the key and value to 'pr_body' in a markdown format,
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# except for the items containing the word 'walkthrough'
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pr_body = ""
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for idx, (key, value) in enumerate(data.items()):
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pr_body += f"## {key}:\n"
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if 'walkthrough' in key.lower():
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# for filename, description in value.items():
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for file in value:
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filename = file['filename'].replace("'", "`")
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description = file['changes in file']
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pr_body += f'`{filename}`: {description}\n'
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else:
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# if the value is a list, join its items by comma
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if type(value) == list:
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value = ', '.join(v for v in value)
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pr_body += f"{value}\n"
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if idx < len(data) - 1:
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pr_body += "\n___\n"
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if get_settings().config.verbosity_level >= 2:
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logging.info(f"title:\n{title}\n{pr_body}")
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return title, pr_body, pr_types, markdown_text |