xiaxinhang/pr-agent
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refactor: remove reflection and incremental review features from docs and code

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mrT23
2024-12-25 08:21:33 +02:00
parent 495c1ebe5f
commit 5e1b04980e
4 changed files with 0 additions and 160 deletions
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3
pr_agent/agent/pr_agent.py
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@ -13,7 +13,6 @@ from pr_agent.tools.pr_config import PRConfig
from pr_agent.tools.pr_description import PRDescription
from pr_agent.tools.pr_generate_labels import PRGenerateLabels
from pr_agent.tools.pr_help_message import PRHelpMessage
from pr_agent.tools.pr_information_from_user import PRInformationFromUser
from pr_agent.tools.pr_line_questions import PR_LineQuestions
from pr_agent.tools.pr_questions import PRQuestions
from pr_agent.tools.pr_reviewer import PRReviewer
@ -25,8 +24,6 @@ command2class = {
"answer": PRReviewer,
"review": PRReviewer,
"review_pr": PRReviewer,
"reflect": PRInformationFromUser,
"reflect_and_review": PRInformationFromUser,
"describe": PRDescription,
"describe_pr": PRDescription,
"improve": PRCodeSuggestions,

79
pr_agent/tools/pr_information_from_user.py
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@ -1,79 +0,0 @@
import copy
from functools import partial
from jinja2 import Environment, StrictUndefined
from pr_agent.algo.ai_handlers.base_ai_handler import BaseAiHandler
from pr_agent.algo.ai_handlers.litellm_ai_handler import LiteLLMAIHandler
from pr_agent.algo.pr_processing import get_pr_diff, retry_with_fallback_models
from pr_agent.algo.token_handler import TokenHandler
from pr_agent.config_loader import get_settings
from pr_agent.git_providers import get_git_provider
from pr_agent.git_providers.git_provider import get_main_pr_language
from pr_agent.log import get_logger
class PRInformationFromUser:
def __init__(self, pr_url: str, args: list = None,
ai_handler: partial[BaseAiHandler,] = LiteLLMAIHandler):
self.git_provider = get_git_provider()(pr_url)
self.main_pr_language = get_main_pr_language(
self.git_provider.get_languages(), self.git_provider.get_files()
)
self.ai_handler = ai_handler()
self.ai_handler.main_pr_language = self.main_pr_language
self.vars = {
"title": self.git_provider.pr.title,
"branch": self.git_provider.get_pr_branch(),
"description": self.git_provider.get_pr_description(),
"language": self.main_pr_language,
"diff": "", # empty diff for initial calculation
"commit_messages_str": self.git_provider.get_commit_messages(),
}
self.token_handler = TokenHandler(self.git_provider.pr,
self.vars,
get_settings().pr_information_from_user_prompt.system,
get_settings().pr_information_from_user_prompt.user)
self.patches_diff = None
self.prediction = None
async def run(self):
get_logger().info('Generating question to the user...')
if get_settings().config.publish_output:
self.git_provider.publish_comment("Preparing questions...", is_temporary=True)
await retry_with_fallback_models(self._prepare_prediction)
get_logger().info('Preparing questions...')
pr_comment = self._prepare_pr_answer()
if get_settings().config.publish_output:
get_logger().info('Pushing questions...')
self.git_provider.publish_comment(pr_comment)
self.git_provider.remove_initial_comment()
return ""
async def _prepare_prediction(self, model):
get_logger().info('Getting PR diff...')
self.patches_diff = get_pr_diff(self.git_provider, self.token_handler, model)
get_logger().info('Getting AI prediction...')
self.prediction = await self._get_prediction(model)
async def _get_prediction(self, model: str):
variables = copy.deepcopy(self.vars)
variables["diff"] = self.patches_diff # update diff
environment = Environment(undefined=StrictUndefined)
system_prompt = environment.from_string(get_settings().pr_information_from_user_prompt.system).render(variables)
user_prompt = environment.from_string(get_settings().pr_information_from_user_prompt.user).render(variables)
if get_settings().config.verbosity_level >= 2:
get_logger().info(f"\nSystem prompt:\n{system_prompt}")
get_logger().info(f"\nUser prompt:\n{user_prompt}")
response, finish_reason = await self.ai_handler.chat_completion(
model=model, temperature=get_settings().config.temperature, system=system_prompt, user=user_prompt)
return response
def _prepare_pr_answer(self) -> str:
model_output = self.prediction.strip()
if get_settings().config.verbosity_level >= 2:
get_logger().info(f"answer_str:\n{model_output}")
answer_str = f"{model_output}\n\n Please respond to the questions above in the following format:\n\n" +\
"\n>/answer\n>1) ...\n>2) ...\n>...\n"
return answer_str