refactor(ai_handler): move streaming response handling and Azure token generation to helpers

pr_agent/algo/ai_handlers/litellm_ai_handler.py

@@ -7,29 +7,14 @@ from tenacity import retry, retry_if_exception_type, retry_if_not_exception_type
 
 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.ai_handlers.litellm_helpers import _handle_streaming_response, MockResponse, _get_azure_ad_token, \
+    _process_litellm_extra_body
 from pr_agent.algo.utils import ReasoningEffort, get_version
 from pr_agent.config_loader import get_settings
 from pr_agent.log import get_logger
 import json
 
-OPENAI_RETRIES = 5
+MODEL_RETRIES = 2
-
-
-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):
@@ -127,7 +112,7 @@ class LiteLLMAIHandler(BaseAiHandler):
         if get_settings().get("AZURE_AD.CLIENT_ID", None):
             self.azure = True
             # Generate access token using Azure AD credentials from settings
-            access_token = self._get_azure_ad_token()
+            access_token = _get_azure_ad_token()
             litellm.api_key = access_token
             openai.api_key = access_token
             
@@ -163,26 +148,6 @@ class LiteLLMAIHandler(BaseAiHandler):
         # 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.
-        Returns:
-            str: The access token
-        """
-        from azure.identity import ClientSecretCredential
-        try:
-            credential = ClientSecretCredential(
-                tenant_id=get_settings().azure_ad.tenant_id,
-                client_id=get_settings().azure_ad.client_id,
-                client_secret=get_settings().azure_ad.client_secret
-            )
-            # Get token for Azure OpenAI service
-            token = credential.get_token("https://cognitiveservices.azure.com/.default")
-            return token.token
-        except Exception as e:
-            get_logger().error(f"Failed to get Azure AD token: {e}")
-            raise
-
     def prepare_logs(self, response, system, user, resp, finish_reason):
         response_log = response.dict().copy()
         response_log['system'] = system
@@ -195,37 +160,6 @@ class LiteLLMAIHandler(BaseAiHandler):
             response_log['main_pr_language'] = 'unknown'
         return response_log
 
-    def _process_litellm_extra_body(self, kwargs: dict) -> dict:
-        """
-        Process LITELLM.EXTRA_BODY configuration and update kwargs accordingly.
-        
-        Args:
-            kwargs: The current kwargs dictionary to update
-            
-        Returns:
-            Updated kwargs dictionary
-            
-        Raises:
-            ValueError: If extra_body contains invalid JSON, unsupported keys, or colliding keys
-        """
-        allowed_extra_body_keys = {"processing_mode", "service_tier"}
-        extra_body = getattr(getattr(get_settings(), "litellm", None), "extra_body", None)
-        if extra_body:
-            try:
-                litellm_extra_body = json.loads(extra_body)
-                if not isinstance(litellm_extra_body, dict):
-                    raise ValueError("LITELLM.EXTRA_BODY must be a JSON object")
-                unsupported_keys = set(litellm_extra_body.keys()) - allowed_extra_body_keys
-                if unsupported_keys:
-                    raise ValueError(f"LITELLM.EXTRA_BODY contains unsupported keys: {', '.join(unsupported_keys)}. Allowed keys: {', '.join(allowed_extra_body_keys)}")
-                colliding_keys = kwargs.keys() & litellm_extra_body.keys()
-                if colliding_keys:
-                    raise ValueError(f"LITELLM.EXTRA_BODY cannot override existing parameters: {', '.join(colliding_keys)}")
-                kwargs.update(litellm_extra_body)
-            except json.JSONDecodeError as e:
-                raise ValueError(f"LITELLM.EXTRA_BODY contains invalid JSON: {str(e)}")
-        return kwargs
-
     def _configure_claude_extended_thinking(self, model: str, kwargs: dict) -> dict:
         """
         Configure Claude extended thinking parameters if applicable.
@@ -326,7 +260,7 @@ class LiteLLMAIHandler(BaseAiHandler):
 
     @retry(
         retry=retry_if_exception_type(openai.APIError) & retry_if_not_exception_type(openai.RateLimitError),
-        stop=stop_after_attempt(OPENAI_RETRIES),
+        stop=stop_after_attempt(MODEL_RETRIES),
     )
     async def chat_completion(self, model: str, system: str, user: str, temperature: float = 0.2, img_path: str = None):
         try:
@@ -416,7 +350,7 @@ class LiteLLMAIHandler(BaseAiHandler):
                 kwargs["extra_headers"] = litellm_extra_headers
 
             # Support for custom OpenAI body fields (e.g., Flex Processing)
-            kwargs = self._process_litellm_extra_body(kwargs)
+            kwargs = _process_litellm_extra_body(kwargs)
 
             get_logger().debug("Prompts", artifact={"system": system, "user": user})
 
@@ -449,41 +383,6 @@ class LiteLLMAIHandler(BaseAiHandler):
 
         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.
@@ -492,7 +391,7 @@ class LiteLLMAIHandler(BaseAiHandler):
             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)
+            resp, finish_reason = await _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

pr_agent/algo/ai_handlers/litellm_helpers.py

@@ -0,0 +1,113 @@
+import json
+
+import openai
+from azure.identity import ClientSecretCredential
+
+from pr_agent.config_loader import get_settings
+from pr_agent.log import get_logger
+
+
+async def _handle_streaming_response(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
+
+
+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
+
+
+def _get_azure_ad_token():
+    """
+    Generates an access token using Azure AD credentials from settings.
+    Returns:
+        str: The access token
+    """
+    from azure.identity import ClientSecretCredential
+    try:
+        credential = ClientSecretCredential(
+            tenant_id=get_settings().azure_ad.tenant_id,
+            client_id=get_settings().azure_ad.client_id,
+            client_secret=get_settings().azure_ad.client_secret
+        )
+        # Get token for Azure OpenAI service
+        token = credential.get_token("https://cognitiveservices.azure.com/.default")
+        return token.token
+    except Exception as e:
+        get_logger().error(f"Failed to get Azure AD token: {e}")
+        raise
+
+
+def _process_litellm_extra_body(kwargs: dict) -> dict:
+    """
+    Process LITELLM.EXTRA_BODY configuration and update kwargs accordingly.
+
+    Args:
+        kwargs: The current kwargs dictionary to update
+
+    Returns:
+        Updated kwargs dictionary
+
+    Raises:
+        ValueError: If extra_body contains invalid JSON, unsupported keys, or colliding keys
+    """
+    allowed_extra_body_keys = {"processing_mode", "service_tier"}
+    extra_body = getattr(getattr(get_settings(), "litellm", None), "extra_body", None)
+    if extra_body:
+        try:
+            litellm_extra_body = json.loads(extra_body)
+            if not isinstance(litellm_extra_body, dict):
+                raise ValueError("LITELLM.EXTRA_BODY must be a JSON object")
+            unsupported_keys = set(litellm_extra_body.keys()) - allowed_extra_body_keys
+            if unsupported_keys:
+                raise ValueError(f"LITELLM.EXTRA_BODY contains unsupported keys: {', '.join(unsupported_keys)}. Allowed keys: {', '.join(allowed_extra_body_keys)}")
+            colliding_keys = kwargs.keys() & litellm_extra_body.keys()
+            if colliding_keys:
+                raise ValueError(f"LITELLM.EXTRA_BODY cannot override existing parameters: {', '.join(colliding_keys)}")
+            kwargs.update(litellm_extra_body)
+        except json.JSONDecodeError as e:
+            raise ValueError(f"LITELLM.EXTRA_BODY contains invalid JSON: {str(e)}")
+    return kwargs