mirror of
https://github.com/qodo-ai/pr-agent.git
synced 2025-07-02 20:00:41 +08:00
337 lines
19 KiB
Python
337 lines
19 KiB
Python
import copy
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import re
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from functools import partial
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from pathlib import Path
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from jinja2 import Environment, StrictUndefined
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from pr_agent.algo import MAX_TOKENS
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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 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, clip_tokens, load_yaml, get_max_tokens
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from pr_agent.config_loader import get_settings
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from pr_agent.git_providers import BitbucketServerProvider, GithubProvider, get_git_provider_with_context
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from pr_agent.log import get_logger
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def extract_header(snippet):
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res = ''
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lines = snippet.split('===Snippet content===')[0].split('\n')
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highest_header = ''
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highest_level = float('inf')
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for line in lines[::-1]:
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line = line.strip()
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if line.startswith('Header '):
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highest_header = line.split(': ')[1]
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if highest_header:
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res = f"#{highest_header.lower().replace(' ', '-')}"
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return res
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class PRHelpMessage:
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def __init__(self, pr_url: str, args=None, ai_handler: partial[BaseAiHandler,] = LiteLLMAIHandler, return_as_string=False):
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self.git_provider = get_git_provider_with_context(pr_url)
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self.ai_handler = ai_handler()
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self.question_str = self.parse_args(args)
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self.return_as_string = return_as_string
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if self.question_str:
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self.vars = {
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"question": self.question_str,
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"snippets": "",
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}
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self.token_handler = TokenHandler(None,
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self.vars,
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get_settings().pr_help_prompts.system,
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get_settings().pr_help_prompts.user)
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async def _prepare_prediction(self, model: str):
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try:
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variables = copy.deepcopy(self.vars)
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environment = Environment(undefined=StrictUndefined)
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system_prompt = environment.from_string(get_settings().pr_help_prompts.system).render(variables)
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user_prompt = environment.from_string(get_settings().pr_help_prompts.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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return response
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except Exception as e:
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get_logger().error(f"Error while preparing prediction: {e}")
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return ""
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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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def format_markdown_header(self, header: str) -> str:
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try:
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# First, strip common characters from both ends
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cleaned = header.strip('# 💎\n')
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# Define all characters to be removed/replaced in a single pass
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replacements = {
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"'": '',
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"`": '',
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'(': '',
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')': '',
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',': '',
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'.': '',
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'?': '',
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'!': '',
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' ': '-'
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}
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# Compile regex pattern for characters to remove
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pattern = re.compile('|'.join(map(re.escape, replacements.keys())))
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# Perform replacements in a single pass and convert to lowercase
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return pattern.sub(lambda m: replacements[m.group()], cleaned).lower()
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except Exception:
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get_logger().exception(f"Error while formatting markdown header", artifacts={'header': header})
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return ""
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async def run(self):
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try:
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if self.question_str:
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get_logger().info(f'Answering a PR question about the PR {self.git_provider.pr_url} ')
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if not get_settings().get('openai.key'):
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if get_settings().config.publish_output:
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self.git_provider.publish_comment(
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"The `Help` tool chat feature requires an OpenAI API key for calculating embeddings")
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else:
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get_logger().error("The `Help` tool chat feature requires an OpenAI API key for calculating embeddings")
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return
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# current path
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docs_path= Path(__file__).parent.parent.parent / 'docs' / 'docs'
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# get all the 'md' files inside docs_path and its subdirectories
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md_files = list(docs_path.glob('**/*.md'))
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folders_to_exclude = ['/finetuning_benchmark/']
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files_to_exclude = {'EXAMPLE_BEST_PRACTICE.md', 'compression_strategy.md', '/docs/overview/index.md'}
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md_files = [file for file in md_files if not any(folder in str(file) for folder in folders_to_exclude) and not any(file.name == file_to_exclude for file_to_exclude in files_to_exclude)]
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# sort the 'md_files' so that 'priority_files' will be at the top
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priority_files_strings = ['/docs/index.md', '/usage-guide', 'tools/describe.md', 'tools/review.md',
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'tools/improve.md', '/faq']
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md_files_priority = [file for file in md_files if
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any(priority_string in str(file) for priority_string in priority_files_strings)]
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md_files_not_priority = [file for file in md_files if file not in md_files_priority]
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md_files = md_files_priority + md_files_not_priority
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docs_prompt = ""
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for file in md_files:
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try:
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with open(file, 'r') as f:
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file_path = str(file).replace(str(docs_path), '')
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docs_prompt += f"\n==file name==\n\n{file_path}\n\n==file content==\n\n{f.read().strip()}\n=========\n\n"
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except Exception as e:
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get_logger().error(f"Error while reading the file {file}: {e}")
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token_count = self.token_handler.count_tokens(docs_prompt)
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get_logger().debug(f"Token count of full documentation website: {token_count}")
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model = get_settings().config.model
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if model in MAX_TOKENS:
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max_tokens_full = MAX_TOKENS[model] # note - here we take the actual max tokens, without any reductions. we do aim to get the full documentation website in the prompt
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else:
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max_tokens_full = get_max_tokens(model)
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delta_output = 2000
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if token_count > max_tokens_full - delta_output:
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get_logger().info(f"Token count {token_count} exceeds the limit {max_tokens_full - delta_output}. Skipping the PR Help message.")
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docs_prompt = clip_tokens(docs_prompt, max_tokens_full - delta_output)
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self.vars['snippets'] = docs_prompt.strip()
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# run the AI model
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response = await retry_with_fallback_models(self._prepare_prediction, model_type=ModelType.REGULAR)
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response_yaml = load_yaml(response)
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if isinstance(response_yaml, str):
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get_logger().warning(f"failing to parse response: {response_yaml}, publishing the response as is")
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if get_settings().config.publish_output:
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answer_str = f"### Question: \n{self.question_str}\n\n"
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answer_str += f"### Answer:\n\n"
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answer_str += response_yaml
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self.git_provider.publish_comment(answer_str)
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return ""
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response_str = response_yaml.get('response')
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relevant_sections = response_yaml.get('relevant_sections')
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if not relevant_sections:
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get_logger().info(f"Could not find relevant answer for the question: {self.question_str}")
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if get_settings().config.publish_output:
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answer_str = f"### Question: \n{self.question_str}\n\n"
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answer_str += f"### Answer:\n\n"
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answer_str += f"Could not find relevant information to answer the question. Please provide more details and try again."
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self.git_provider.publish_comment(answer_str)
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return ""
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# prepare the answer
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answer_str = ""
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if response_str:
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answer_str += f"### Question: \n{self.question_str}\n\n"
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answer_str += f"### Answer:\n{response_str.strip()}\n\n"
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answer_str += f"#### Relevant Sources:\n\n"
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base_path = "https://qodo-merge-docs.qodo.ai/"
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for section in relevant_sections:
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file = section.get('file_name').strip().removesuffix('.md')
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if str(section['relevant_section_header_string']).strip():
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markdown_header = self.format_markdown_header(section['relevant_section_header_string'])
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answer_str += f"> - {base_path}{file}#{markdown_header}\n"
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else:
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answer_str += f"> - {base_path}{file}\n"
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# publish the answer
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if get_settings().config.publish_output:
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self.git_provider.publish_comment(answer_str)
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else:
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get_logger().info(f"Answer:\n{answer_str}")
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else:
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if not isinstance(self.git_provider, BitbucketServerProvider) and not self.git_provider.is_supported("gfm_markdown"):
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self.git_provider.publish_comment(
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"The `Help` tool requires gfm markdown, which is not supported by your code platform.")
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return
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get_logger().info('Getting PR Help Message...')
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relevant_configs = {'pr_help': dict(get_settings().pr_help),
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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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pr_comment = "## PR Agent Walkthrough 🤖\n\n"
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pr_comment += "Welcome to the PR Agent, an AI-powered tool for automated pull request analysis, feedback, suggestions and more."""
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pr_comment += "\n\nHere is a list of tools you can use to interact with the PR Agent:\n"
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base_path = "https://pr-agent-docs.codium.ai/tools"
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tool_names = []
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tool_names.append(f"[DESCRIBE]({base_path}/describe/)")
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tool_names.append(f"[REVIEW]({base_path}/review/)")
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tool_names.append(f"[IMPROVE]({base_path}/improve/)")
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tool_names.append(f"[UPDATE CHANGELOG]({base_path}/update_changelog/)")
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tool_names.append(f"[HELP DOCS]({base_path}/help_docs/)")
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tool_names.append(f"[ADD DOCS]({base_path}/documentation/) 💎")
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tool_names.append(f"[TEST]({base_path}/test/) 💎")
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tool_names.append(f"[IMPROVE COMPONENT]({base_path}/improve_component/) 💎")
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tool_names.append(f"[ANALYZE]({base_path}/analyze/) 💎")
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tool_names.append(f"[ASK]({base_path}/ask/)")
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tool_names.append(f"[GENERATE CUSTOM LABELS]({base_path}/custom_labels/) 💎")
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tool_names.append(f"[CI FEEDBACK]({base_path}/ci_feedback/) 💎")
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tool_names.append(f"[CUSTOM PROMPT]({base_path}/custom_prompt/) 💎")
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tool_names.append(f"[IMPLEMENT]({base_path}/implement/) 💎")
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descriptions = []
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descriptions.append("Generates PR description - title, type, summary, code walkthrough and labels")
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descriptions.append("Adjustable feedback about the PR, possible issues, security concerns, review effort and more")
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descriptions.append("Code suggestions for improving the PR")
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descriptions.append("Automatically updates the changelog")
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descriptions.append("Answers a question regarding this repository, or a given one, based on given documentation path")
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descriptions.append("Generates documentation to methods/functions/classes that changed in the PR")
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descriptions.append("Generates unit tests for a specific component, based on the PR code change")
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descriptions.append("Code suggestions for a specific component that changed in the PR")
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descriptions.append("Identifies code components that changed in the PR, and enables to interactively generate tests, docs, and code suggestions for each component")
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descriptions.append("Answering free-text questions about the PR")
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descriptions.append("Automatically retrieves and presents similar issues")
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descriptions.append("Generates custom labels for the PR, based on specific guidelines defined by the user")
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descriptions.append("Generates feedback and analysis for a failed CI job")
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descriptions.append("Generates custom suggestions for improving the PR code, derived only from a specific guidelines prompt defined by the user")
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descriptions.append("Generates implementation code from review suggestions")
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commands =[]
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commands.append("`/describe`")
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commands.append("`/review`")
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commands.append("`/improve`")
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commands.append("`/update_changelog`")
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commands.append("`/help_docs`")
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commands.append("`/add_docs`")
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commands.append("`/test`")
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commands.append("`/improve_component`")
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commands.append("`/analyze`")
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commands.append("`/ask`")
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commands.append("`/generate_labels`")
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commands.append("`/checks`")
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commands.append("`/custom_prompt`")
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commands.append("`/implement`")
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checkbox_list = []
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checkbox_list.append(" - [ ] Run <!-- /describe -->")
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checkbox_list.append(" - [ ] Run <!-- /review -->")
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checkbox_list.append(" - [ ] Run <!-- /improve -->")
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checkbox_list.append(" - [ ] Run <!-- /update_changelog -->")
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checkbox_list.append(" - [ ] Run <!-- /help_docs -->")
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checkbox_list.append(" - [ ] Run <!-- /add_docs -->")
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checkbox_list.append(" - [ ] Run <!-- /test -->")
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checkbox_list.append(" - [ ] Run <!-- /improve_component -->")
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checkbox_list.append(" - [ ] Run <!-- /analyze -->")
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checkbox_list.append("[*]")
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checkbox_list.append("[*]")
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checkbox_list.append("[*]")
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checkbox_list.append("[*]")
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checkbox_list.append("[*]")
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checkbox_list.append("[*]")
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checkbox_list.append("[*]")
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checkbox_list.append("[*]")
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checkbox_list.append("[*]")
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if isinstance(self.git_provider, GithubProvider) and not get_settings().config.get('disable_checkboxes', False):
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pr_comment += f"<table><tr align='left'><th align='left'>Tool</th><th align='left'>Description</th><th align='left'>Trigger Interactively :gem:</th></tr>"
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for i in range(len(tool_names)):
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pr_comment += f"\n<tr><td align='left'>\n\n<strong>{tool_names[i]}</strong></td>\n<td>{descriptions[i]}</td>\n<td>\n\n{checkbox_list[i]}\n</td></tr>"
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pr_comment += "</table>\n\n"
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pr_comment += f"""\n\n(1) Note that each tool can be [triggered automatically](https://pr-agent-docs.codium.ai/usage-guide/automations_and_usage/#github-app-automatic-tools-when-a-new-pr-is-opened) when a new PR is opened, or called manually by [commenting on a PR](https://pr-agent-docs.codium.ai/usage-guide/automations_and_usage/#online-usage)."""
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pr_comment += f"""\n\n(2) Tools marked with [*] require additional parameters to be passed. For example, to invoke the `/ask` tool, you need to comment on a PR: `/ask "<question content>"`. See the relevant documentation for each tool for more details."""
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elif isinstance(self.git_provider, BitbucketServerProvider):
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# only support basic commands in BBDC
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pr_comment = generate_bbdc_table(tool_names[:4], descriptions[:4])
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else:
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pr_comment += f"<table><tr align='left'><th align='left'>Tool</th><th align='left'>Command</th><th align='left'>Description</th></tr>"
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for i in range(len(tool_names)):
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pr_comment += f"\n<tr><td align='left'>\n\n<strong>{tool_names[i]}</strong></td><td>{commands[i]}</td><td>{descriptions[i]}</td></tr>"
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pr_comment += "</table>\n\n"
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pr_comment += f"""\n\nNote that each tool can be [invoked automatically](https://pr-agent-docs.codium.ai/usage-guide/automations_and_usage/) when a new PR is opened, or called manually by [commenting on a PR](https://pr-agent-docs.codium.ai/usage-guide/automations_and_usage/#online-usage)."""
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if get_settings().config.publish_output:
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self.git_provider.publish_comment(pr_comment)
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except Exception as e:
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get_logger().exception(f"Error while running PRHelpMessage: {e}")
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return ""
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async def prepare_relevant_snippets(self, sim_results):
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# Get relevant snippets
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relevant_snippets_full = []
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relevant_pages_full = []
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relevant_snippets_full_header = []
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th = 0.75
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for s in sim_results:
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page = s[0].metadata['source']
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content = s[0].page_content
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score = s[1]
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relevant_snippets_full.append(content)
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relevant_snippets_full_header.append(extract_header(content))
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relevant_pages_full.append(page)
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# build the snippets string
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relevant_snippets_str = ""
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for i, s in enumerate(relevant_snippets_full):
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relevant_snippets_str += f"Snippet {i+1}:\n\n{s}\n\n"
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relevant_snippets_str += "-------------------\n\n"
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return relevant_pages_full, relevant_snippets_full_header, relevant_snippets_str
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def generate_bbdc_table(column_arr_1, column_arr_2):
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# Generating header row
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header_row = "| Tool | Description | \n"
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# Generating separator row
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separator_row = "|--|--|\n"
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# Generating data rows
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data_rows = ""
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max_len = max(len(column_arr_1), len(column_arr_2))
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for i in range(max_len):
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col1 = column_arr_1[i] if i < len(column_arr_1) else ""
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col2 = column_arr_2[i] if i < len(column_arr_2) else ""
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data_rows += f"| {col1} | {col2} |\n"
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# Combine all parts to form the complete table
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markdown_table = header_row + separator_row + data_rows
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return markdown_table
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