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refactor(ai_handler): compact streaming path to reduce main flow impact

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Makonike
2025-07-13 22:37:14 +08:00
parent 74df3f8bd5
commit 11fb6ccc7e
1 changed files with 23 additions and 20 deletions
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43
pr_agent/algo/ai_handlers/litellm_ai_handler.py
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@ -390,20 +390,8 @@ class LiteLLMAIHandler(BaseAiHandler):
get_logger().info(f"\nSystem prompt:\n{system}") get_logger().info(f"\nSystem prompt:\n{system}")
get_logger().info(f"\nUser prompt:\n{user}") get_logger().info(f"\nUser prompt:\n{user}")
# Check if model requires streaming # Get completion with automatic streaming detection
if model in self.streaming_required_models: resp, finish_reason, response_obj = await self._get_completion(model, **kwargs)
kwargs["stream"] = True
get_logger().info(f"Using streaming mode for model {model}")
response = await acompletion(**kwargs)
# Handle streaming response
resp, finish_reason = await self._handle_streaming_response(response)
else:
response = await acompletion(**kwargs)
# Handle non-streaming response
if response is None or len(response["choices"]) == 0:
raise openai.APIError
resp = response["choices"][0]['message']['content']
finish_reason = response["choices"][0]["finish_reason"]
except openai.RateLimitError as e: except openai.RateLimitError as e:
get_logger().error(f"Rate limit error during LLM inference: {e}") get_logger().error(f"Rate limit error during LLM inference: {e}")
@ -418,12 +406,7 @@ class LiteLLMAIHandler(BaseAiHandler):
get_logger().debug(f"\nAI response:\n{resp}") get_logger().debug(f"\nAI response:\n{resp}")
# log the full response for debugging # log the full response for debugging
if model in self.streaming_required_models: response_log = self.prepare_logs(response_obj, system, user, resp, finish_reason)
# for streaming, we don't have the full response object, so we create a mock one
mock_response = MockResponse(resp, finish_reason)
response_log = self.prepare_logs(mock_response, system, user, resp, finish_reason)
else:
response_log = self.prepare_logs(response, system, user, resp, finish_reason)
get_logger().debug("Full_response", artifact=response_log) get_logger().debug("Full_response", artifact=response_log)
# for CLI debugging # for CLI debugging
@ -466,3 +449,23 @@ class LiteLLMAIHandler(BaseAiHandler):
get_logger().debug(f"Streaming response resulted in empty content but completed with finish_reason: {finish_reason}") get_logger().debug(f"Streaming response resulted in empty content but completed with finish_reason: {finish_reason}")
return full_response, finish_reason return full_response, finish_reason
async def _get_completion(self, model, **kwargs):
"""
Wrapper that automatically handles streaming for required models.
"""
if model in self.streaming_required_models:
kwargs["stream"] = True
get_logger().info(f"Using streaming mode for model {model}")
response = await acompletion(**kwargs)
resp, finish_reason = await self._handle_streaming_response(response)
# Create MockResponse for streaming since we don't have the full response object
mock_response = MockResponse(resp, finish_reason)
return resp, finish_reason, mock_response
else:
response = await acompletion(**kwargs)
if response is None or len(response["choices"]) == 0:
raise openai.APIError
return (response["choices"][0]['message']['content'],
response["choices"][0]["finish_reason"],
response)