mirror of
https://github.com/qodo-ai/pr-agent.git
synced 2025-07-01 19:30:40 +08:00
785 lines
44 KiB
Python
785 lines
44 KiB
Python
import asyncio
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import copy
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import difflib
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import re
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import textwrap
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from functools import partial
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from typing import Dict, List
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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 (add_ai_metadata_to_diff_files,
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get_pr_diff, get_pr_multi_diffs,
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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, load_yaml, replace_code_tags,
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show_relevant_configurations)
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from pr_agent.config_loader import get_settings
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from pr_agent.git_providers import (AzureDevopsProvider, GithubProvider,
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GitLabProvider, get_git_provider,
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get_git_provider_with_context)
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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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from pr_agent.tools.pr_description import insert_br_after_x_chars
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class PRCodeSuggestions:
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def __init__(self, pr_url: str, cli_mode=False, args: list = None,
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ai_handler: partial[BaseAiHandler,] = LiteLLMAIHandler):
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self.git_provider = get_git_provider_with_context(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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# limit context specifically for the improve command, which has hard input to parse:
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if get_settings().pr_code_suggestions.max_context_tokens:
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MAX_CONTEXT_TOKENS_IMPROVE = get_settings().pr_code_suggestions.max_context_tokens
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if get_settings().config.max_model_tokens > MAX_CONTEXT_TOKENS_IMPROVE:
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get_logger().info(f"Setting max_model_tokens to {MAX_CONTEXT_TOKENS_IMPROVE} for PR improve")
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get_settings().config.max_model_tokens_original = get_settings().config.max_model_tokens
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get_settings().config.max_model_tokens = MAX_CONTEXT_TOKENS_IMPROVE
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# extended mode
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try:
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self.is_extended = self._get_is_extended(args or [])
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except:
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self.is_extended = False
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num_code_suggestions = get_settings().pr_code_suggestions.num_code_suggestions_per_chunk
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self.ai_handler = ai_handler()
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self.ai_handler.main_pr_language = self.main_language
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self.patches_diff = None
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self.prediction = None
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self.pr_url = pr_url
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self.cli_mode = cli_mode
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self.pr_description, self.pr_description_files = (
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self.git_provider.get_pr_description(split_changes_walkthrough=True))
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if (self.pr_description_files and get_settings().get("config.is_auto_command", False) and
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get_settings().get("config.enable_ai_metadata", False)):
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add_ai_metadata_to_diff_files(self.git_provider, self.pr_description_files)
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get_logger().debug(f"AI metadata added to the this command")
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else:
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get_settings().set("config.enable_ai_metadata", False)
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get_logger().debug(f"AI metadata is disabled for this command")
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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.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": 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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"relevant_best_practices": "",
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"is_ai_metadata": get_settings().get("config.enable_ai_metadata", False),
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}
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self.pr_code_suggestions_prompt_system = get_settings().pr_code_suggestions_prompt.system
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self.token_handler = TokenHandler(self.git_provider.pr,
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self.vars,
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self.pr_code_suggestions_prompt_system,
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get_settings().pr_code_suggestions_prompt.user)
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self.progress = f"## Generating PR code suggestions\n\n"
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self.progress += f"""\nWork in progress ...<br>\n<img src="https://codium.ai/images/pr_agent/dual_ball_loading-crop.gif" width=48>"""
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self.progress_response = None
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async def run(self):
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try:
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if not self.git_provider.get_files():
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get_logger().info(f"PR has no files: {self.pr_url}, skipping code suggestions")
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return None
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get_logger().info('Generating code suggestions for PR...')
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relevant_configs = {'pr_code_suggestions': dict(get_settings().pr_code_suggestions),
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'config': dict(get_settings().config)}
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get_logger().debug("Relevant configs", artifacts=relevant_configs)
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if (get_settings().config.publish_output and get_settings().config.publish_output_progress and
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not get_settings().config.get('is_auto_command', False)):
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if self.git_provider.is_supported("gfm_markdown"):
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self.progress_response = self.git_provider.publish_comment(self.progress)
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else:
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self.git_provider.publish_comment("Preparing suggestions...", is_temporary=True)
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if not self.is_extended:
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data = await retry_with_fallback_models(self._prepare_prediction)
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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 data:
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data = {"code_suggestions": []}
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if (data is None or 'code_suggestions' not in data or not data['code_suggestions']
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and get_settings().config.publish_output):
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get_logger().warning('No code suggestions found for the PR.')
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pr_body = "## PR Code Suggestions ✨\n\nNo code suggestions found for the PR."
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get_logger().debug(f"PR output", artifact=pr_body)
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if self.progress_response:
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self.git_provider.edit_comment(self.progress_response, body=pr_body)
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else:
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self.git_provider.publish_comment(pr_body)
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return
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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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get_logger().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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self.git_provider.remove_initial_comment()
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if ((not get_settings().pr_code_suggestions.commitable_code_suggestions) and
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self.git_provider.is_supported("gfm_markdown")):
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# generate summarized suggestions
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pr_body = self.generate_summarized_suggestions(data)
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get_logger().debug(f"PR output", artifact=pr_body)
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# require self-review
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if get_settings().pr_code_suggestions.demand_code_suggestions_self_review:
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text = get_settings().pr_code_suggestions.code_suggestions_self_review_text
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pr_body += f"\n\n- [ ] {text}"
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if get_settings().pr_code_suggestions.approve_pr_on_self_review:
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pr_body += ' <!-- approve pr self-review -->'
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# add usage guide
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if (get_settings().pr_code_suggestions.enable_chat_text and get_settings().config.is_auto_command
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and isinstance(self.git_provider, GithubProvider)):
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pr_body += "\n\n>💡 Need additional feedback ? start a [PR chat](https://chromewebstore.google.com/detail/ephlnjeghhogofkifjloamocljapahnl) \n\n"
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if get_settings().pr_code_suggestions.enable_help_text:
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pr_body += "<hr>\n\n<details> <summary><strong>💡 Tool usage guide:</strong></summary><hr> \n\n"
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pr_body += HelpMessage.get_improve_usage_guide()
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pr_body += "\n</details>\n"
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# Output the relevant configurations if enabled
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if get_settings().get('config', {}).get('output_relevant_configurations', False):
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pr_body += show_relevant_configurations(relevant_section='pr_code_suggestions')
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# publish the PR comment
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if get_settings().pr_code_suggestions.persistent_comment:
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final_update_message = False
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self.publish_persistent_comment_with_history(pr_body,
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initial_header="## PR Code Suggestions ✨",
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update_header=True,
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name="suggestions",
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final_update_message=final_update_message,
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max_previous_comments=get_settings().pr_code_suggestions.max_history_len,
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progress_response=self.progress_response)
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else:
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if self.progress_response:
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self.git_provider.edit_comment(self.progress_response, body=pr_body)
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else:
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self.git_provider.publish_comment(pr_body)
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# dual publishing mode
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if int(get_settings().pr_code_suggestions.dual_publishing_score_threshold) > 0:
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data_above_threshold = {'code_suggestions': []}
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try:
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for suggestion in data['code_suggestions']:
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if int(suggestion.get('score', 0)) >= int(get_settings().pr_code_suggestions.dual_publishing_score_threshold) \
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and suggestion.get('improved_code'):
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data_above_threshold['code_suggestions'].append(suggestion)
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if not data_above_threshold['code_suggestions'][-1]['existing_code']:
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get_logger().info(f'Identical existing and improved code for dual publishing found')
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data_above_threshold['code_suggestions'][-1]['existing_code'] = suggestion[
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'improved_code']
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if data_above_threshold['code_suggestions']:
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get_logger().info(
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f"Publishing {len(data_above_threshold['code_suggestions'])} suggestions in dual publishing mode")
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self.push_inline_code_suggestions(data_above_threshold)
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except Exception as e:
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get_logger().error(f"Failed to publish dual publishing suggestions, error: {e}")
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else:
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self.push_inline_code_suggestions(data)
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if self.progress_response:
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self.git_provider.remove_comment(self.progress_response)
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else:
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get_logger().info('Code suggestions generated for PR, but not published since publish_output is False.')
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except Exception as e:
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get_logger().error(f"Failed to generate code suggestions for PR, error: {e}")
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if get_settings().config.publish_output:
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if self.progress_response:
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self.progress_response.delete()
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else:
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try:
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self.git_provider.remove_initial_comment()
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self.git_provider.publish_comment(f"Failed to generate code suggestions for PR")
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except Exception as e:
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pass
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def publish_persistent_comment_with_history(self, pr_comment: str,
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initial_header: str,
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update_header: bool = True,
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name='review',
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final_update_message=True,
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max_previous_comments=4,
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progress_response=None):
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if isinstance(self.git_provider, AzureDevopsProvider): # get_latest_commit_url is not supported yet
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if progress_response:
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self.git_provider.edit_comment(progress_response, pr_comment)
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else:
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self.git_provider.publish_comment(pr_comment)
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return
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history_header = f"#### Previous suggestions\n"
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last_commit_num = self.git_provider.get_latest_commit_url().split('/')[-1][:7]
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latest_suggestion_header = f"Latest suggestions up to {last_commit_num}"
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latest_commit_html_comment = f"<!-- {last_commit_num} -->"
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found_comment = None
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if max_previous_comments > 0:
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try:
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prev_comments = list(self.git_provider.get_issue_comments())
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for comment in prev_comments:
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if comment.body.startswith(initial_header):
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prev_suggestions = comment.body
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found_comment = comment
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comment_url = self.git_provider.get_comment_url(comment)
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if history_header.strip() not in comment.body:
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# no history section
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# extract everything between <table> and </table> in comment.body including <table> and </table>
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table_index = comment.body.find("<table>")
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if table_index == -1:
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self.git_provider.edit_comment(comment, pr_comment)
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continue
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# find http link from comment.body[:table_index]
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up_to_commit_txt = self.extract_link(comment.body[:table_index])
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prev_suggestion_table = comment.body[
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table_index:comment.body.rfind("</table>") + len("</table>")]
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tick = "✅ " if "✅" in prev_suggestion_table else ""
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# surround with details tag
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prev_suggestion_table = f"<details><summary>{tick}{name.capitalize()}{up_to_commit_txt}</summary>\n<br>{prev_suggestion_table}\n\n</details>"
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new_suggestion_table = pr_comment.replace(initial_header, "").strip()
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pr_comment_updated = f"{initial_header}\n{latest_commit_html_comment}\n\n"
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pr_comment_updated += f"{latest_suggestion_header}\n{new_suggestion_table}\n\n___\n\n"
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pr_comment_updated += f"{history_header}{prev_suggestion_table}\n"
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else:
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# get the text of the previous suggestions until the latest commit
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sections = prev_suggestions.split(history_header.strip())
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latest_table = sections[0].strip()
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prev_suggestion_table = sections[1].replace(history_header, "").strip()
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# get text after the latest_suggestion_header in comment.body
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table_ind = latest_table.find("<table>")
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up_to_commit_txt = self.extract_link(latest_table[:table_ind])
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latest_table = latest_table[table_ind:latest_table.rfind("</table>") + len("</table>")]
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# enforce max_previous_comments
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count = prev_suggestions.count(f"\n<details><summary>{name.capitalize()}")
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count += prev_suggestions.count(f"\n<details><summary>✅ {name.capitalize()}")
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if count >= max_previous_comments:
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# remove the oldest suggestion
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prev_suggestion_table = prev_suggestion_table[:prev_suggestion_table.rfind(
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f"<details><summary>{name.capitalize()} up to commit")]
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tick = "✅ " if "✅" in latest_table else ""
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# Add to the prev_suggestions section
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last_prev_table = f"\n<details><summary>{tick}{name.capitalize()}{up_to_commit_txt}</summary>\n<br>{latest_table}\n\n</details>"
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prev_suggestion_table = last_prev_table + "\n" + prev_suggestion_table
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new_suggestion_table = pr_comment.replace(initial_header, "").strip()
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pr_comment_updated = f"{initial_header}\n"
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pr_comment_updated += f"{latest_commit_html_comment}\n\n"
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pr_comment_updated += f"{latest_suggestion_header}\n\n{new_suggestion_table}\n\n"
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pr_comment_updated += "___\n\n"
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pr_comment_updated += f"{history_header}\n"
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pr_comment_updated += f"{prev_suggestion_table}\n"
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get_logger().info(f"Persistent mode - updating comment {comment_url} to latest {name} message")
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if progress_response: # publish to 'progress_response' comment, because it refreshes immediately
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self.git_provider.edit_comment(progress_response, pr_comment_updated)
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self.git_provider.remove_comment(comment)
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else:
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self.git_provider.edit_comment(comment, pr_comment_updated)
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return
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except Exception as e:
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get_logger().exception(f"Failed to update persistent review, error: {e}")
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pass
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# if we are here, we did not find a previous comment to update
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body = pr_comment.replace(initial_header, "").strip()
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pr_comment = f"{initial_header}\n\n{latest_commit_html_comment}\n\n{body}\n\n"
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if progress_response:
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self.git_provider.edit_comment(progress_response, pr_comment)
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else:
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self.git_provider.publish_comment(pr_comment)
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def extract_link(self, s):
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r = re.compile(r"<!--.*?-->")
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match = r.search(s)
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up_to_commit_txt = ""
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if match:
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up_to_commit_txt = f" up to commit {match.group(0)[4:-3].strip()}"
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return up_to_commit_txt
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async def _prepare_prediction(self, model: str) -> dict:
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self.patches_diff = get_pr_diff(self.git_provider,
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self.token_handler,
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model,
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add_line_numbers_to_hunks=True,
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disable_extra_lines=False)
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if self.patches_diff:
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get_logger().debug(f"PR diff", artifact=self.patches_diff)
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self.prediction = await self._get_prediction(model, self.patches_diff)
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else:
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get_logger().warning(f"Empty PR diff")
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self.prediction = None
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data = self.prediction
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return data
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async def _get_prediction(self, model: str, patches_diff: str) -> dict:
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variables = copy.deepcopy(self.vars)
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variables["diff"] = patches_diff # update diff
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environment = Environment(undefined=StrictUndefined)
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system_prompt = environment.from_string(self.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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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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# load suggestions from the AI response
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data = self._prepare_pr_code_suggestions(response)
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# self-reflect on suggestions
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if get_settings().pr_code_suggestions.self_reflect_on_suggestions:
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model_turbo = get_settings().config.model_turbo # use turbo model for self-reflection, since it is an easier task
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response_reflect = await self.self_reflect_on_suggestions(data["code_suggestions"],
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patches_diff, model=model_turbo)
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if response_reflect:
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response_reflect_yaml = load_yaml(response_reflect)
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code_suggestions_feedback = response_reflect_yaml["code_suggestions"]
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if len(code_suggestions_feedback) == len(data["code_suggestions"]):
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for i, suggestion in enumerate(data["code_suggestions"]):
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try:
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suggestion["score"] = code_suggestions_feedback[i]["suggestion_score"]
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suggestion["score_why"] = code_suggestions_feedback[i]["why"]
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except Exception as e: #
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get_logger().error(f"Error processing suggestion score {i}",
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artifact={"suggestion": suggestion,
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"code_suggestions_feedback": code_suggestions_feedback[i]})
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suggestion["score"] = 7
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suggestion["score_why"] = ""
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else:
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# get_logger().error(f"Could not self-reflect on suggestions. using default score 7")
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for i, suggestion in enumerate(data["code_suggestions"]):
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suggestion["score"] = 7
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suggestion["score_why"] = ""
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return data
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@staticmethod
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def _truncate_if_needed(suggestion):
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max_code_suggestion_length = get_settings().get("PR_CODE_SUGGESTIONS.MAX_CODE_SUGGESTION_LENGTH", 0)
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suggestion_truncation_message = get_settings().get("PR_CODE_SUGGESTIONS.SUGGESTION_TRUNCATION_MESSAGE", "")
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if max_code_suggestion_length > 0:
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if len(suggestion['improved_code']) > max_code_suggestion_length:
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suggestion['improved_code'] = suggestion['improved_code'][:max_code_suggestion_length]
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suggestion['improved_code'] += f"\n{suggestion_truncation_message}"
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get_logger().info(f"Truncated suggestion from {len(suggestion['improved_code'])} "
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f"characters to {max_code_suggestion_length} characters")
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return suggestion
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def _prepare_pr_code_suggestions(self, predictions: str) -> Dict:
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data = load_yaml(predictions.strip(),
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keys_fix_yaml=["relevant_file", "suggestion_content", "existing_code", "improved_code"],
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first_key="code_suggestions", last_key="label")
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if isinstance(data, list):
|
|
data = {'code_suggestions': data}
|
|
|
|
# remove or edit invalid suggestions
|
|
suggestion_list = []
|
|
one_sentence_summary_list = []
|
|
for i, suggestion in enumerate(data['code_suggestions']):
|
|
try:
|
|
needed_keys = ['one_sentence_summary', 'label', 'relevant_file', 'relevant_lines_start',
|
|
'relevant_lines_end']
|
|
is_valid_keys = True
|
|
for key in needed_keys:
|
|
if key not in suggestion:
|
|
is_valid_keys = False
|
|
get_logger().debug(
|
|
f"Skipping suggestion {i + 1}, because it does not contain '{key}':\n'{suggestion}")
|
|
break
|
|
if not is_valid_keys:
|
|
continue
|
|
|
|
if suggestion['one_sentence_summary'] in one_sentence_summary_list:
|
|
get_logger().debug(f"Skipping suggestion {i + 1}, because it is a duplicate: {suggestion}")
|
|
continue
|
|
|
|
if 'const' in suggestion['suggestion_content'] and 'instead' in suggestion[
|
|
'suggestion_content'] and 'let' in suggestion['suggestion_content']:
|
|
get_logger().debug(
|
|
f"Skipping suggestion {i + 1}, because it uses 'const instead let': {suggestion}")
|
|
continue
|
|
|
|
if ('existing_code' in suggestion) and ('improved_code' in suggestion):
|
|
if suggestion['existing_code'] == suggestion['improved_code']:
|
|
get_logger().debug(
|
|
f"edited improved suggestion {i + 1}, because equal to existing code: {suggestion['existing_code']}")
|
|
if get_settings().pr_code_suggestions.commitable_code_suggestions:
|
|
suggestion['improved_code'] = "" # we need 'existing_code' to locate the code in the PR
|
|
else:
|
|
suggestion['existing_code'] = ""
|
|
suggestion = self._truncate_if_needed(suggestion)
|
|
one_sentence_summary_list.append(suggestion['one_sentence_summary'])
|
|
suggestion_list.append(suggestion)
|
|
else:
|
|
get_logger().info(
|
|
f"Skipping suggestion {i + 1}, because it does not contain 'existing_code' or 'improved_code': {suggestion}")
|
|
except Exception as e:
|
|
get_logger().error(f"Error processing suggestion {i + 1}: {suggestion}, error: {e}")
|
|
data['code_suggestions'] = suggestion_list
|
|
|
|
return data
|
|
|
|
def push_inline_code_suggestions(self, data):
|
|
code_suggestions = []
|
|
|
|
if not data['code_suggestions']:
|
|
get_logger().info('No suggestions found to improve this PR.')
|
|
if self.progress_response:
|
|
return self.git_provider.edit_comment(self.progress_response,
|
|
body='No suggestions found to improve this PR.')
|
|
else:
|
|
return self.git_provider.publish_comment('No suggestions found to improve this PR.')
|
|
|
|
for d in data['code_suggestions']:
|
|
try:
|
|
if get_settings().config.verbosity_level >= 2:
|
|
get_logger().info(f"suggestion: {d}")
|
|
relevant_file = d['relevant_file'].strip()
|
|
relevant_lines_start = int(d['relevant_lines_start']) # absolute position
|
|
relevant_lines_end = int(d['relevant_lines_end'])
|
|
content = d['suggestion_content'].rstrip()
|
|
new_code_snippet = d['improved_code'].rstrip()
|
|
label = d['label'].strip()
|
|
|
|
if new_code_snippet:
|
|
new_code_snippet = self.dedent_code(relevant_file, relevant_lines_start, new_code_snippet)
|
|
|
|
if d.get('score'):
|
|
body = f"**Suggestion:** {content} [{label}, importance: {d.get('score')}]\n```suggestion\n" + new_code_snippet + "\n```"
|
|
else:
|
|
body = f"**Suggestion:** {content} [{label}]\n```suggestion\n" + new_code_snippet + "\n```"
|
|
code_suggestions.append({'body': body, 'relevant_file': relevant_file,
|
|
'relevant_lines_start': relevant_lines_start,
|
|
'relevant_lines_end': relevant_lines_end,
|
|
'original_suggestion': d})
|
|
except Exception:
|
|
get_logger().info(f"Could not parse suggestion: {d}")
|
|
|
|
is_successful = self.git_provider.publish_code_suggestions(code_suggestions)
|
|
if not is_successful:
|
|
get_logger().info("Failed to publish code suggestions, trying to publish each suggestion separately")
|
|
for code_suggestion in code_suggestions:
|
|
self.git_provider.publish_code_suggestions([code_suggestion])
|
|
|
|
def dedent_code(self, relevant_file, relevant_lines_start, new_code_snippet):
|
|
try: # dedent code snippet
|
|
self.diff_files = self.git_provider.diff_files if self.git_provider.diff_files \
|
|
else self.git_provider.get_diff_files()
|
|
original_initial_line = None
|
|
for file in self.diff_files:
|
|
if file.filename.strip() == relevant_file:
|
|
if file.head_file:
|
|
file_lines = file.head_file.splitlines()
|
|
if relevant_lines_start > len(file_lines):
|
|
get_logger().warning(
|
|
"Could not dedent code snippet, because relevant_lines_start is out of range",
|
|
artifact={'filename': file.filename,
|
|
'file_content': file.head_file,
|
|
'relevant_lines_start': relevant_lines_start,
|
|
'new_code_snippet': new_code_snippet})
|
|
return new_code_snippet
|
|
else:
|
|
original_initial_line = file_lines[relevant_lines_start - 1]
|
|
else:
|
|
get_logger().warning("Could not dedent code snippet, because head_file is missing",
|
|
artifact={'filename': file.filename,
|
|
'relevant_lines_start': relevant_lines_start,
|
|
'new_code_snippet': new_code_snippet})
|
|
return new_code_snippet
|
|
break
|
|
if original_initial_line:
|
|
suggested_initial_line = new_code_snippet.splitlines()[0]
|
|
original_initial_spaces = len(original_initial_line) - len(original_initial_line.lstrip())
|
|
suggested_initial_spaces = len(suggested_initial_line) - len(suggested_initial_line.lstrip())
|
|
delta_spaces = original_initial_spaces - suggested_initial_spaces
|
|
if delta_spaces > 0:
|
|
new_code_snippet = textwrap.indent(new_code_snippet, delta_spaces * " ").rstrip('\n')
|
|
except Exception as e:
|
|
get_logger().error(f"Error when dedenting code snippet for file {relevant_file}, error: {e}")
|
|
|
|
return new_code_snippet
|
|
|
|
def _get_is_extended(self, args: list[str]) -> bool:
|
|
"""Check if extended mode should be enabled by the `--extended` flag or automatically according to the configuration"""
|
|
if any(["extended" in arg for arg in args]):
|
|
get_logger().info("Extended mode is enabled by the `--extended` flag")
|
|
return True
|
|
if get_settings().pr_code_suggestions.auto_extended_mode:
|
|
# get_logger().info("Extended mode is enabled automatically based on the configuration toggle")
|
|
return True
|
|
return False
|
|
|
|
async def _prepare_prediction_extended(self, model: str) -> dict:
|
|
self.patches_diff_list = get_pr_multi_diffs(self.git_provider, self.token_handler, model,
|
|
max_calls=get_settings().pr_code_suggestions.max_number_of_calls)
|
|
if self.patches_diff_list:
|
|
get_logger().info(f"Number of PR chunk calls: {len(self.patches_diff_list)}")
|
|
get_logger().debug(f"PR diff:", artifact=self.patches_diff_list)
|
|
|
|
# parallelize calls to AI:
|
|
if get_settings().pr_code_suggestions.parallel_calls:
|
|
prediction_list = await asyncio.gather(
|
|
*[self._get_prediction(model, patches_diff) for patches_diff in self.patches_diff_list])
|
|
self.prediction_list = prediction_list
|
|
else:
|
|
prediction_list = []
|
|
for i, patches_diff in enumerate(self.patches_diff_list):
|
|
prediction = await self._get_prediction(model, patches_diff)
|
|
prediction_list.append(prediction)
|
|
|
|
data = {"code_suggestions": []}
|
|
for j, predictions in enumerate(prediction_list): # each call adds an element to the list
|
|
if "code_suggestions" in predictions:
|
|
score_threshold = max(1, int(get_settings().pr_code_suggestions.suggestions_score_threshold))
|
|
for i, prediction in enumerate(predictions["code_suggestions"]):
|
|
try:
|
|
if get_settings().pr_code_suggestions.self_reflect_on_suggestions:
|
|
score = int(prediction.get("score", 1))
|
|
if score >= score_threshold:
|
|
data["code_suggestions"].append(prediction)
|
|
else:
|
|
get_logger().info(
|
|
f"Removing suggestions {i} from call {j}, because score is {score}, and score_threshold is {score_threshold}",
|
|
artifact=prediction)
|
|
else:
|
|
data["code_suggestions"].append(prediction)
|
|
except Exception as e:
|
|
get_logger().error(f"Error getting PR diff for suggestion {i} in call {j}, error: {e}")
|
|
self.data = data
|
|
else:
|
|
get_logger().warning(f"Empty PR diff list")
|
|
self.data = data = None
|
|
return data
|
|
|
|
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 = []
|
|
if not data:
|
|
return suggestion_list
|
|
for suggestion in data:
|
|
suggestion_list.append(suggestion)
|
|
data_sorted = [[]] * len(suggestion_list)
|
|
|
|
if len(suggestion_list) == 1:
|
|
return 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)
|
|
response, finish_reason = await self.ai_handler.chat_completion(model=model, system=system_prompt,
|
|
user=user_prompt)
|
|
|
|
sort_order = load_yaml(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_code_suggestions.final_clip_factor != 1:
|
|
max_len = max(
|
|
len(data_sorted),
|
|
get_settings().pr_code_suggestions.num_code_suggestions_per_chunk,
|
|
)
|
|
new_len = int(0.5 + max_len * get_settings().pr_code_suggestions.final_clip_factor)
|
|
if new_len < len(data_sorted):
|
|
data_sorted = data_sorted[:new_len]
|
|
except Exception as e:
|
|
if get_settings().config.verbosity_level >= 1:
|
|
get_logger().info(f"Could not sort suggestions, error: {e}")
|
|
data_sorted = suggestion_list
|
|
|
|
return data_sorted
|
|
|
|
def generate_summarized_suggestions(self, data: Dict) -> str:
|
|
try:
|
|
pr_body = "## PR Code Suggestions ✨\n\n"
|
|
|
|
if len(data.get('code_suggestions', [])) == 0:
|
|
pr_body += "No suggestions found to improve this PR."
|
|
return pr_body
|
|
|
|
if get_settings().pr_code_suggestions.enable_intro_text and get_settings().config.is_auto_command:
|
|
pr_body += "Explore these optional code suggestions:\n\n"
|
|
|
|
language_extension_map_org = get_settings().language_extension_map_org
|
|
extension_to_language = {}
|
|
for language, extensions in language_extension_map_org.items():
|
|
for ext in extensions:
|
|
extension_to_language[ext] = language
|
|
|
|
pr_body += "<table>"
|
|
header = f"Suggestion"
|
|
delta = 66
|
|
header += " " * delta
|
|
if get_settings().pr_code_suggestions.self_reflect_on_suggestions:
|
|
pr_body += f"""<thead><tr><td>Category</td><td align=left>{header}</td><td align=center>Score</td></tr>"""
|
|
else:
|
|
pr_body += f"""<thead><tr><td>Category</td><td align=left>{header}</td></tr>"""
|
|
pr_body += """<tbody>"""
|
|
suggestions_labels = dict()
|
|
# add all suggestions related to each label
|
|
for suggestion in data['code_suggestions']:
|
|
label = suggestion['label'].strip().strip("'").strip('"')
|
|
if label not in suggestions_labels:
|
|
suggestions_labels[label] = []
|
|
suggestions_labels[label].append(suggestion)
|
|
|
|
# sort suggestions_labels by the suggestion with the highest score
|
|
if get_settings().pr_code_suggestions.self_reflect_on_suggestions:
|
|
suggestions_labels = dict(
|
|
sorted(suggestions_labels.items(), key=lambda x: max([s['score'] for s in x[1]]), reverse=True))
|
|
# sort the suggestions inside each label group by score
|
|
for label, suggestions in suggestions_labels.items():
|
|
suggestions_labels[label] = sorted(suggestions, key=lambda x: x['score'], reverse=True)
|
|
|
|
counter_suggestions = 0
|
|
for label, suggestions in suggestions_labels.items():
|
|
num_suggestions = len(suggestions)
|
|
pr_body += f"""<tr><td rowspan={num_suggestions}><strong>{label.capitalize()}</strong></td>\n"""
|
|
for i, suggestion in enumerate(suggestions):
|
|
|
|
relevant_file = suggestion['relevant_file'].strip()
|
|
relevant_lines_start = int(suggestion['relevant_lines_start'])
|
|
relevant_lines_end = int(suggestion['relevant_lines_end'])
|
|
range_str = ""
|
|
if relevant_lines_start == relevant_lines_end:
|
|
range_str = f"[{relevant_lines_start}]"
|
|
else:
|
|
range_str = f"[{relevant_lines_start}-{relevant_lines_end}]"
|
|
|
|
try:
|
|
code_snippet_link = self.git_provider.get_line_link(relevant_file, relevant_lines_start,
|
|
relevant_lines_end)
|
|
except:
|
|
code_snippet_link = ""
|
|
# add html table for each suggestion
|
|
|
|
suggestion_content = suggestion['suggestion_content'].rstrip()
|
|
CHAR_LIMIT_PER_LINE = 84
|
|
suggestion_content = insert_br_after_x_chars(suggestion_content, CHAR_LIMIT_PER_LINE)
|
|
# pr_body += f"<tr><td><details><summary>{suggestion_content}</summary>"
|
|
existing_code = suggestion['existing_code'].rstrip() + "\n"
|
|
improved_code = suggestion['improved_code'].rstrip() + "\n"
|
|
|
|
diff = difflib.unified_diff(existing_code.split('\n'),
|
|
improved_code.split('\n'), n=999)
|
|
patch_orig = "\n".join(diff)
|
|
patch = "\n".join(patch_orig.splitlines()[5:]).strip('\n')
|
|
|
|
example_code = ""
|
|
example_code += f"```diff\n{patch.rstrip()}\n```\n"
|
|
if i == 0:
|
|
pr_body += f"""<td>\n\n"""
|
|
else:
|
|
pr_body += f"""<tr><td>\n\n"""
|
|
suggestion_summary = suggestion['one_sentence_summary'].strip().rstrip('.')
|
|
if "'<" in suggestion_summary and ">'" in suggestion_summary:
|
|
# escape the '<' and '>' characters, otherwise they are interpreted as html tags
|
|
get_logger().info(f"Escaped suggestion summary: {suggestion_summary}")
|
|
suggestion_summary = suggestion_summary.replace("'<", "`<")
|
|
suggestion_summary = suggestion_summary.replace(">'", ">`")
|
|
if '`' in suggestion_summary:
|
|
suggestion_summary = replace_code_tags(suggestion_summary)
|
|
|
|
pr_body += f"""\n\n<details><summary>{suggestion_summary}</summary>\n\n___\n\n"""
|
|
pr_body += f"""
|
|
**{suggestion_content}**
|
|
|
|
[{relevant_file} {range_str}]({code_snippet_link})
|
|
|
|
{example_code.rstrip()}
|
|
"""
|
|
if get_settings().pr_code_suggestions.self_reflect_on_suggestions:
|
|
pr_body += f"<details><summary>Suggestion importance[1-10]: {suggestion['score']}</summary>\n\n"
|
|
pr_body += f"Why: {suggestion['score_why']}\n\n"
|
|
pr_body += f"</details>"
|
|
|
|
pr_body += f"</details>"
|
|
|
|
# # add another column for 'score'
|
|
if get_settings().pr_code_suggestions.self_reflect_on_suggestions:
|
|
pr_body += f"</td><td align=center>{suggestion['score']}\n\n"
|
|
|
|
pr_body += f"</td></tr>"
|
|
counter_suggestions += 1
|
|
|
|
# pr_body += "</details>"
|
|
# pr_body += """</td></tr>"""
|
|
pr_body += """</tr></tbody></table>"""
|
|
return pr_body
|
|
except Exception as e:
|
|
get_logger().info(f"Failed to publish summarized code suggestions, error: {e}")
|
|
return ""
|
|
|
|
async def self_reflect_on_suggestions(self, suggestion_list: List, patches_diff: str, model: str) -> str:
|
|
if not suggestion_list:
|
|
return ""
|
|
|
|
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,
|
|
"diff": patches_diff,
|
|
'num_code_suggestions': len(suggestion_list),
|
|
"is_ai_metadata": get_settings().get("config.enable_ai_metadata", False)}
|
|
environment = Environment(undefined=StrictUndefined)
|
|
system_prompt_reflect = environment.from_string(
|
|
get_settings().pr_code_suggestions_reflect_prompt.system).render(
|
|
variables)
|
|
user_prompt_reflect = environment.from_string(
|
|
get_settings().pr_code_suggestions_reflect_prompt.user).render(variables)
|
|
with get_logger().contextualize(command="self_reflect_on_suggestions"):
|
|
response_reflect, finish_reason_reflect = await self.ai_handler.chat_completion(model=model,
|
|
system=system_prompt_reflect,
|
|
user=user_prompt_reflect)
|
|
except Exception as e:
|
|
get_logger().info(f"Could not reflect on suggestions, error: {e}")
|
|
return ""
|
|
return response_reflect
|