mirror of
https://github.com/qodo-ai/pr-agent.git
synced 2025-07-02 11:50:37 +08:00
181 lines
7.0 KiB
Python
181 lines
7.0 KiB
Python
import copy
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import re
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from functools import partial
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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_handlers.base_ai_handler import BaseAiHandler
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from pr_agent.algo.ai_handlers.litellm_ai_handler import LiteLLMAIHandler
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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 get_user_labels, load_yaml, set_custom_labels
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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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from pr_agent.log import get_logger
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class PRGenerateLabels:
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def __init__(self, pr_url: str, args: list = None,
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ai_handler: partial[BaseAiHandler,] = LiteLLMAIHandler):
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"""
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Initialize the PRGenerateLabels object with the necessary attributes and objects for generating labels
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corresponding to the PR 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 PRGenerateLabels 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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self.pr_id = self.git_provider.get_pr_id()
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# Initialize the AI handler
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self.ai_handler = ai_handler()
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self.ai_handler.main_pr_language = self.main_pr_language
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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(full=False),
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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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"enable_custom_labels": get_settings().config.enable_custom_labels,
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"custom_labels_class": "", # will be filled if necessary in 'set_custom_labels' function
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}
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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_custom_labels_prompt.system,
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get_settings().pr_custom_labels_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 labels using an AI model and publishes it to the PR.
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"""
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try:
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get_logger().info(f"Generating a PR labels {self.pr_id}")
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if get_settings().config.publish_output:
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self.git_provider.publish_comment("Preparing PR labels...", is_temporary=True)
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await retry_with_fallback_models(self._prepare_prediction)
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get_logger().info(f"Preparing answer {self.pr_id}")
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if self.prediction:
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self._prepare_data()
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else:
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return None
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pr_labels = self._prepare_labels()
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if get_settings().config.publish_output:
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get_logger().info(f"Pushing labels {self.pr_id}")
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current_labels = self.git_provider.get_pr_labels()
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user_labels = get_user_labels(current_labels)
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pr_labels = pr_labels + user_labels
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if self.git_provider.is_supported("get_labels"):
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self.git_provider.publish_labels(pr_labels)
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elif pr_labels:
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value = ', '.join(v for v in pr_labels)
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pr_labels_text = f"## PR Labels:\n{value}\n"
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self.git_provider.publish_comment(pr_labels_text, is_temporary=False)
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self.git_provider.remove_initial_comment()
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except Exception as e:
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get_logger().error(f"Error generating PR labels {self.pr_id}: {e}")
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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 labels 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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get_logger().info(f"Getting PR diff {self.pr_id}")
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self.patches_diff = get_pr_diff(self.git_provider, self.token_handler, model)
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get_logger().info(f"Getting AI prediction {self.pr_id}")
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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 labels 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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set_custom_labels(variables, self.git_provider)
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self.variables = variables
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system_prompt = environment.from_string(get_settings().pr_custom_labels_prompt.system).render(self.variables)
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user_prompt = environment.from_string(get_settings().pr_custom_labels_prompt.user).render(self.variables)
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response, finish_reason = await self.ai_handler.chat_completion(
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model=model,
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temperature=get_settings().config.temperature,
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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_data(self):
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# Load the AI prediction data into a dictionary
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self.data = load_yaml(self.prediction.strip())
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def _prepare_labels(self) -> List[str]:
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pr_types = []
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# If the 'labels' key is present in the dictionary, split its value by comma and assign it to 'pr_types'
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if 'labels' in self.data:
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if type(self.data['labels']) == list:
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pr_types = self.data['labels']
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elif type(self.data['labels']) == str:
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pr_types = self.data['labels'].split(',')
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pr_types = [label.strip() for label in pr_types]
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# convert lowercase labels to original case
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try:
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if "labels_minimal_to_labels_dict" in self.variables:
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d: dict = self.variables["labels_minimal_to_labels_dict"]
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for i, label_i in enumerate(pr_types):
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if label_i in d:
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pr_types[i] = d[label_i]
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except Exception as e:
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get_logger().error(f"Error converting labels to original case {self.pr_id}: {e}")
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return pr_types
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