Merge pull request #1925 from Makonike/feature_only_streaming_model_support

feat: Support Only Streaming Model

pr_agent/algo/__init__.py

@@ -45,6 +45,7 @@ MAX_TOKENS = {
     'command-nightly': 4096,
     'deepseek/deepseek-chat': 128000,  # 128K, but may be limited by config.max_model_tokens
     'deepseek/deepseek-reasoner': 64000,  # 64K, but may be limited by config.max_model_tokens
+    'openai/qwq-plus': 131072,  # 131K context length, but may be limited by config.max_model_tokens
     'replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1': 4096,
     'meta-llama/Llama-2-7b-chat-hf': 4096,
     'vertex_ai/codechat-bison': 6144,
@@ -193,3 +194,8 @@ CLAUDE_EXTENDED_THINKING_MODELS = [
     "anthropic/claude-3-7-sonnet-20250219",
     "claude-3-7-sonnet-20250219"
 ]
+
+# Models that require streaming mode
+STREAMING_REQUIRED_MODELS = [
+    "openai/qwq-plus"
+]

pr_agent/algo/ai_handlers/litellm_ai_handler.py

@@ -5,7 +5,7 @@ import requests
 from litellm import acompletion
 from tenacity import retry, retry_if_exception_type, retry_if_not_exception_type, stop_after_attempt
 
-from pr_agent.algo import CLAUDE_EXTENDED_THINKING_MODELS, NO_SUPPORT_TEMPERATURE_MODELS, SUPPORT_REASONING_EFFORT_MODELS, USER_MESSAGE_ONLY_MODELS
+from pr_agent.algo import CLAUDE_EXTENDED_THINKING_MODELS, NO_SUPPORT_TEMPERATURE_MODELS, SUPPORT_REASONING_EFFORT_MODELS, USER_MESSAGE_ONLY_MODELS, STREAMING_REQUIRED_MODELS
 from pr_agent.algo.ai_handlers.base_ai_handler import BaseAiHandler
 from pr_agent.algo.utils import ReasoningEffort, get_version
 from pr_agent.config_loader import get_settings
@@ -15,6 +15,23 @@ import json
 OPENAI_RETRIES = 5
 
 
+class MockResponse:
+    """Mock response object for streaming models to enable consistent logging."""
+
+    def __init__(self, resp, finish_reason):
+        self._data = {
+            "choices": [
+                {
+                    "message": {"content": resp},
+                    "finish_reason": finish_reason
+                }
+            ]
+        }
+
+    def dict(self):
+        return self._data
+
+
 class LiteLLMAIHandler(BaseAiHandler):
     """
     This class handles interactions with the OpenAI API for chat completions.
@@ -143,6 +160,9 @@ class LiteLLMAIHandler(BaseAiHandler):
         # Models that support extended thinking
         self.claude_extended_thinking_models = CLAUDE_EXTENDED_THINKING_MODELS
 
+        # Models that require streaming
+        self.streaming_required_models = STREAMING_REQUIRED_MODELS
+
     def _get_azure_ad_token(self):
         """
         Generates an access token using Azure AD credentials from settings.
@@ -404,7 +424,9 @@ class LiteLLMAIHandler(BaseAiHandler):
                 get_logger().info(f"\nSystem prompt:\n{system}")
                 get_logger().info(f"\nUser prompt:\n{user}")
 
-            response = await acompletion(**kwargs)
+            # Get completion with automatic streaming detection
+            resp, finish_reason, response_obj = await self._get_completion(model, **kwargs)
+
         except openai.RateLimitError as e:
             get_logger().error(f"Rate limit error during LLM inference: {e}")
             raise
@@ -414,15 +436,11 @@ class LiteLLMAIHandler(BaseAiHandler):
         except Exception as e:
             get_logger().warning(f"Unknown error during LLM inference: {e}")
             raise openai.APIError from e
-        if response is None or len(response["choices"]) == 0:
+
-            raise openai.APIError
-        else:
-            resp = response["choices"][0]['message']['content']
-            finish_reason = response["choices"][0]["finish_reason"]
         get_logger().debug(f"\nAI response:\n{resp}")
 
         # log the full response for debugging
-            response_log = self.prepare_logs(response, system, user, resp, finish_reason)
+        response_log = self.prepare_logs(response_obj, system, user, resp, finish_reason)
         get_logger().debug("Full_response", artifact=response_log)
 
         # for CLI debugging
@@ -430,3 +448,58 @@ class LiteLLMAIHandler(BaseAiHandler):
             get_logger().info(f"\nAI response:\n{resp}")
 
         return resp, finish_reason
+
+    async def _handle_streaming_response(self, response):
+        """
+        Handle streaming response from acompletion and collect the full response.
+
+        Args:
+            response: The streaming response object from acompletion
+
+        Returns:
+            tuple: (full_response_content, finish_reason)
+        """
+        full_response = ""
+        finish_reason = None
+
+        try:
+            async for chunk in response:
+                if chunk.choices and len(chunk.choices) > 0:
+                    choice = chunk.choices[0]
+                    delta = choice.delta
+                    content = getattr(delta, 'content', None)
+                    if content:
+                        full_response += content
+                    if choice.finish_reason:
+                        finish_reason = choice.finish_reason
+        except Exception as e:
+            get_logger().error(f"Error handling streaming response: {e}")
+            raise
+
+        if not full_response and finish_reason is None:
+            get_logger().warning("Streaming response resulted in empty content with no finish reason")
+            raise openai.APIError("Empty streaming response received without proper completion")
+        elif not full_response and finish_reason:
+            get_logger().debug(f"Streaming response resulted in empty content but completed with finish_reason: {finish_reason}")
+            raise openai.APIError(f"Streaming response completed with finish_reason '{finish_reason}' but no content received")
+        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)