mirror of
https://github.com/qodo-ai/pr-agent.git
synced 2025-07-03 20:30:41 +08:00
188 lines
9.3 KiB
Python
188 lines
9.3 KiB
Python
import argparse
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import copy
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from functools import partial
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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.git_patch_processing import (
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decouple_and_convert_to_hunks_with_lines_numbers, extract_hunk_lines_from_patch)
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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 ModelType
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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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from pr_agent.servers.help import HelpMessage
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class PR_LineQuestions:
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def __init__(self, pr_url: str, args=None, ai_handler: partial[BaseAiHandler,] = LiteLLMAIHandler):
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self.question_str = self.parse_args(args)
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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.ai_handler = ai_handler()
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self.ai_handler.main_pr_language = self.main_pr_language
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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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"diff": "", # empty diff for initial calculation
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"question": self.question_str,
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"full_hunk": "",
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"selected_lines": "",
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"conversation_history": "",
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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_line_questions_prompt.system,
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get_settings().pr_line_questions_prompt.user)
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self.patches_diff = None
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self.prediction = None
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# get settings for use conversation history
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self.use_conversation_history = get_settings().pr_questions.use_conversation_history
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def parse_args(self, args):
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if args and len(args) > 0:
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question_str = " ".join(args)
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else:
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question_str = ""
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return question_str
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async def run(self):
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get_logger().info('Answering a PR lines question...')
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# if get_settings().config.publish_output:
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# self.git_provider.publish_comment("Preparing answer...", is_temporary=True)
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# set conversation history if enabled
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# currently only supports GitHub provider
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if self.use_conversation_history and isinstance(self.git_provider, GithubProvider):
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self._load_conversation_history()
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self.patch_with_lines = ""
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ask_diff = get_settings().get('ask_diff_hunk', "")
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line_start = get_settings().get('line_start', '')
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line_end = get_settings().get('line_end', '')
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side = get_settings().get('side', 'RIGHT')
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file_name = get_settings().get('file_name', '')
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comment_id = get_settings().get('comment_id', '')
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if ask_diff:
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self.patch_with_lines, self.selected_lines = extract_hunk_lines_from_patch(ask_diff,
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file_name,
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line_start=line_start,
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line_end=line_end,
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side=side
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)
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else:
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diff_files = self.git_provider.get_diff_files()
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for file in diff_files:
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if file.filename == file_name:
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self.patch_with_lines, self.selected_lines = extract_hunk_lines_from_patch(file.patch, file.filename,
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line_start=line_start,
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line_end=line_end,
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side=side)
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if self.patch_with_lines:
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model_answer = await retry_with_fallback_models(self._get_prediction, model_type=ModelType.WEAK)
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# sanitize the answer so that no line will start with "/"
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model_answer_sanitized = model_answer.strip().replace("\n/", "\n /")
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if model_answer_sanitized.startswith("/"):
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model_answer_sanitized = " " + model_answer_sanitized
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get_logger().info('Preparing answer...')
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if comment_id:
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self.git_provider.reply_to_comment_from_comment_id(comment_id, model_answer_sanitized)
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else:
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self.git_provider.publish_comment(model_answer_sanitized)
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return ""
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def _load_conversation_history(self):
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"""generate conversation history from the code review thread"""
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try:
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comment_id = get_settings().get('comment_id', '')
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file_path = get_settings().get('file_name', '')
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line_number = get_settings().get('line_end', '')
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# return if no comment id or file path and line number
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if not (comment_id or (file_path and line_number)):
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return
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# initialize conversation history
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conversation_history = []
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if hasattr(self.git_provider, 'get_review_thread_comments') and comment_id:
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try:
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# get review thread comments
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thread_comments = self.git_provider.get_review_thread_comments(comment_id)
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# current question id (this question is excluded from the context)
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current_question_id = comment_id
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# generate conversation history from the comments
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for comment in thread_comments:
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# skip empty comments
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body = getattr(comment, 'body', '')
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if not body or not body.strip():
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continue
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# except for current question
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# except for current question
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if current_question_id and comment.id == current_question_id:
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# remove the AI command (/ask etc) from the beginning of the comment (optional)
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clean_body = body
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if clean_body.startswith('/'):
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clean_body = clean_body.split('\n', 1)[-1] if '\n' in clean_body else ''
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if not clean_body.strip():
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continue
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# author info
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user = comment.user
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author = user.login if hasattr(user, 'login') else 'Unknown'
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# confirm if the author is the current user (AI vs user)
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is_ai = 'bot' in author.lower() or '[bot]' in author.lower()
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role = 'AI' if is_ai else 'User'
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# append to the conversation history
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conversation_history.append(f"{role} ({author}): {clean_body}")
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# transform the conversation history to a string
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if conversation_history:
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self.vars["conversation_history"] = "\n\n".join(conversation_history)
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get_logger().info(f"Loaded {len(conversation_history)} comments from the code review thread")
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else:
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self.vars["conversation_history"] = ""
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except Exception as e:
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get_logger().warning(f"Failed to get review thread comments: {e}")
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self.vars["conversation_history"] = ""
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except Exception as e:
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get_logger().error(f"Error loading conversation history: {e}")
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self.vars["conversation_history"] = ""
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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["full_hunk"] = self.patch_with_lines # update diff
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variables["selected_lines"] = self.selected_lines
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environment = Environment(undefined=StrictUndefined)
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system_prompt = environment.from_string(get_settings().pr_line_questions_prompt.system).render(variables)
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user_prompt = environment.from_string(get_settings().pr_line_questions_prompt.user).render(variables)
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if get_settings().config.verbosity_level >= 2:
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# get_logger().info(f"\nSystem prompt:\n{system_prompt}")
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# get_logger().info(f"\nUser prompt:\n{user_prompt}")
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print(f"\nSystem prompt:\n{system_prompt}")
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print(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, temperature=get_settings().config.temperature, system=system_prompt, user=user_prompt)
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return response
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