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Add truncation and summarization features to PR code suggestions

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This commit is contained in:
mrT23
2024-03-04 08:16:05 +02:00
parent 248c6b13be
commit eed23a7aaa
2 changed files with 37 additions and 9 deletions
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10
pr_agent/settings/pr_code_suggestions_prompts.toml
View File

@ -40,12 +40,14 @@ Specific instructions:
{%- if extra_instructions %}
Extra instructions from the user:
======
{{ extra_instructions }}
======
{%- endif %}
The output must be a YAML object equivalent to type $PRCodeSuggestions, according to the following Pydantic definitions:
=====
class CodeSuggestion(BaseModel):
@ -80,18 +82,18 @@ code_suggestions:
...
{%- if summarize_mode %}
existing_code: |
def func1():
...
improved_code: |
...
one_sentence_summary: |
...
relevant_lines_start: 12
relevant_lines_end: 12
relevant_lines_end: 13
{%- else %}
existing_code: |
def func1():
...
relevant_lines_start: 12
relevant_lines_end: 12
relevant_lines_end: 13
improved_code: |
...
{%- endif %}

34
pr_agent/tools/pr_code_suggestions.py
View File

@ -137,6 +137,7 @@ class PRCodeSuggestions:
model,
add_line_numbers_to_hunks=True,
disable_extra_lines=True)
if self.patches_diff:
get_logger().debug(f"PR diff", artifact=self.patches_diff)
self.prediction = await self._get_prediction(model, self.patches_diff)
@ -150,12 +151,23 @@ class PRCodeSuggestions:
environment = Environment(undefined=StrictUndefined)
system_prompt = environment.from_string(get_settings().pr_code_suggestions_prompt.system).render(variables)
user_prompt = environment.from_string(get_settings().pr_code_suggestions_prompt.user).render(variables)
response, finish_reason = await self.ai_handler.chat_completion(model=model, temperature=0.2,
system=system_prompt, user=user_prompt)
return response
@staticmethod
def _truncate_if_needed(suggestion):
max_code_suggestion_length = get_settings().get("PR_CODE_SUGGESTIONS.MAX_CODE_SUGGESTION_LENGTH", 0)
suggestion_truncation_message = get_settings().get("PR_CODE_SUGGESTIONS.SUGGESTION_TRUNCATION_MESSAGE", "")
if max_code_suggestion_length > 0:
if len(suggestion['improved_code']) > max_code_suggestion_length:
suggestion['improved_code'] = suggestion['improved_code'][:max_code_suggestion_length]
suggestion['improved_code'] += f"\n{suggestion_truncation_message}"
get_logger().info(f"Truncated suggestion from {len(suggestion['improved_code'])} "
f"characters to {max_code_suggestion_length} characters")
return suggestion
def _prepare_pr_code_suggestions(self) -> Dict:
review = self.prediction.strip()
data = load_yaml(review,
@ -165,8 +177,22 @@ class PRCodeSuggestions:
# remove invalid suggestions
suggestion_list = []
one_sentence_summary_list = []
for i, suggestion in enumerate(data['code_suggestions']):
if suggestion['existing_code'] != suggestion['improved_code']:
if get_settings().pr_code_suggestions.summarize:
if not suggestion or 'one_sentence_summary' not in suggestion or 'label' not in suggestion or 'relevant_file' not in suggestion:
get_logger().debug(f"Skipping suggestion {i + 1}, because it is invalid: {suggestion}")
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 ('existing_code' in suggestion) and ('improved_code' in suggestion) and (
suggestion['existing_code'] != suggestion['improved_code']):
suggestion = self._truncate_if_needed(suggestion)
if get_settings().pr_code_suggestions.summarize:
one_sentence_summary_list.append(suggestion['one_sentence_summary'])
suggestion_list.append(suggestion)
else:
get_logger().debug(
@ -250,7 +276,8 @@ class PRCodeSuggestions:
# 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])
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 = []
@ -304,7 +331,6 @@ class PRCodeSuggestions:
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)