xiaxinhang/pr-agent
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mrT23
2024-04-14 12:00:19 +03:00
parent a4680ded93
commit 8f0f08006f
5 changed files with 49 additions and 9 deletions
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29
pr_agent/tools/pr_questions.py
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@ -56,6 +56,12 @@ class PRQuestions:
get_logger().debug("Relevant configs", artifacts=relevant_configs)
if get_settings().config.publish_output:
self.git_provider.publish_comment("Preparing answer...", is_temporary=True)
# identify image
img_path = self.idenfity_image_in_comment()
if img_path:
get_logger().debug(f"Image path identified", artifact=img_path)
await retry_with_fallback_models(self._prepare_prediction)
pr_comment = self._prepare_pr_answer()
@ -71,6 +77,19 @@ class PRQuestions:
self.git_provider.remove_initial_comment()
return ""
def idenfity_image_in_comment(self):
img_path = ''
if '![image]' in self.question_str:
# assuming structure:
# /ask question ... > ![image](img_path)
img_path = self.question_str.split('![image]')[1].strip().strip('()')
self.vars['img_path'] = img_path
elif 'https://' in self.question_str and '.png' in self.question_str: # direct image link
# include https:// in the image path
img_path = 'https://' + self.question_str.split('https://')[1]
self.vars['img_path'] = img_path
return img_path
async def _prepare_prediction(self, model: str):
self.patches_diff = get_pr_diff(self.git_provider, self.token_handler, model)
if self.patches_diff:
@ -86,8 +105,14 @@ class PRQuestions:
environment = Environment(undefined=StrictUndefined)
system_prompt = environment.from_string(get_settings().pr_questions_prompt.system).render(variables)
user_prompt = environment.from_string(get_settings().pr_questions_prompt.user).render(variables)
response, finish_reason = await self.ai_handler.chat_completion(model=model, temperature=0.2,
system=system_prompt, user=user_prompt)
if 'img_path' in variables:
img_path = self.vars['img_path']
response, finish_reason = await self.ai_handler.chat_completion(model=model, temperature=0.2,
system=system_prompt, user=user_prompt,
img_path=img_path)
else:
response, finish_reason = await self.ai_handler.chat_completion(model=model, temperature=0.2,
system=system_prompt, user=user_prompt)
return response
def _prepare_pr_answer(self) -> str: