Reformat

Author: penguine-ip

deepeval/openai_agents/agent.py

@@ -22,6 +22,7 @@
 
 TContext = TypeVar("TContext")
 
+
 class _ObservedModel(Model):
     def __init__(
         self,
@@ -69,12 +70,12 @@ async def get_response(
             span_type="llm",
             func_name="LLM",
             function_kwargs={
-                "system_instructions": system_instructions, 
-                "input": input, 
-                "model_settings": model_settings, 
-                "tools": tools, 
-                "output_schema": output_schema, 
-                "handoffs": handoffs, 
+                "system_instructions": system_instructions,
+                "input": input,
+                "model_settings": model_settings,
+                "tools": tools,
+                "output_schema": output_schema,
+                "handoffs": handoffs,
                 # "tracing": tracing, # not important for llm spans
                 # "previous_response_id": previous_response_id, # not important for llm spans
                 # "conversation_id": conversation_id, # not important for llm spans
@@ -102,7 +103,7 @@ async def get_response(
             llm_span.prompt = self._confident_prompt
 
             observer.result = make_json_serializable(result.output)
-        
+
         return result
 
     def stream_response(
@@ -163,7 +164,9 @@ async def _gen():
                 ):
 
                     if isinstance(event, ResponseCompletedEvent):
-                        observer.result = event.response.output_text #TODO: support other response types
+                        observer.result = (
+                            event.response.output_text
+                        )  # TODO: support other response types
 
                     yield event
 
@@ -177,15 +180,16 @@ async def _gen():
 
         return _gen()
 
+
 @dataclass
 class DeepEvalAgent(BaseAgent[TContext], Generic[TContext]):
     """
     A subclass of agents.Agent.
     """
+
     llm_metric_collection: str = None
     llm_metrics: List[BaseMetric] = None
     confident_prompt: Prompt = None
 
     def __post_init__(self):
         super().__post_init__()
-

deepeval/openai_agents/runner.py

@@ -19,10 +19,12 @@
     from agents.run import AgentRunner
     from agents.run_context import TContext
     from agents.models.interface import Model
+
     agents_available = True
 except:
     agents_available = False
 
+
 def is_agents_available():
     if not agents_available:
         raise ImportError(
@@ -44,33 +46,37 @@ def _patch_default_agent_runner_get_model():
     global _PATCHED_DEFAULT_GET_MODEL
     if _PATCHED_DEFAULT_GET_MODEL:
         return
-    
+
     original_get_model = AgentRunner._get_model
-    
+
     @classmethod
-    def patched_get_model(cls, agent: Agent[Any], run_config: RunConfig) -> Model:
+    def patched_get_model(
+        cls, agent: Agent[Any], run_config: RunConfig
+    ) -> Model:
         model = original_get_model(agent, run_config)
-        
+
         # Extract attributes from agent if it's a DeepEvalAgent
-        llm_metrics = getattr(agent, 'llm_metrics', None)
-        llm_metric_collection = getattr(agent, 'llm_metric_collection', None)
-        confident_prompt = getattr(agent, 'confident_prompt', None)
+        llm_metrics = getattr(agent, "llm_metrics", None)
+        llm_metric_collection = getattr(agent, "llm_metric_collection", None)
+        confident_prompt = getattr(agent, "confident_prompt", None)
         model = _ObservedModel(
             inner=model,
             llm_metric_collection=llm_metric_collection,
             llm_metrics=llm_metrics,
             confident_prompt=confident_prompt,
         )
-        
+
         return model
-    
+
     # Replace the method
     AgentRunner._get_model = patched_get_model
     _PATCHED_DEFAULT_GET_MODEL = True
 
+
 if agents_available:
     _patch_default_agent_runner_get_model()
 
+
 class Runner(AgentsRunner):
 
     @classmethod
@@ -86,15 +92,14 @@ async def run(
         previous_response_id: Optional[str] = None,
         conversation_id: Optional[str] = None,
         session: Optional[Session] = None,
-
         metrics: Optional[List[BaseMetric]] = None,
         metric_collection: Optional[str] = None,
         name: Optional[str] = None,
         tags: Optional[List[str]] = None,
         metadata: Optional[dict] = None,
         thread_id: Optional[str] = None,
         user_id: Optional[str] = None,
-        **kwargs, # backwards compatibility
+        **kwargs,  # backwards compatibility
     ) -> RunResult:
         is_agents_available()
         # _patch_default_agent_runner_get_model()
@@ -131,7 +136,7 @@ async def run(
                 previous_response_id=previous_response_id,
                 conversation_id=conversation_id,
                 session=session,
-                **kwargs, # backwards compatibility
+                **kwargs,  # backwards compatibility
             )
             _output = None
             if thread_id:
@@ -155,7 +160,6 @@ def run_sync(
         previous_response_id: Optional[str] = None,
         conversation_id: Optional[str] = None,
         session: Optional[Session] = None,
-
         metrics: Optional[List[BaseMetric]] = None,
         metric_collection: Optional[str] = None,
         name: Optional[str] = None,
@@ -200,7 +204,7 @@ def run_sync(
                 previous_response_id=previous_response_id,
                 conversation_id=conversation_id,
                 session=session,
-                **kwargs, # backwards compatibility
+                **kwargs,  # backwards compatibility
             )
             _output = None
             if thread_id:
@@ -211,7 +215,7 @@ def run_sync(
             observer.result = _output
 
         return res
-    
+
     @classmethod
     def run_streamed(
         cls,
@@ -225,15 +229,14 @@ def run_streamed(
         previous_response_id: Optional[str] = None,
         conversation_id: Optional[str] = None,
         session: Optional[Session] = None,
-
         metrics: Optional[List[BaseMetric]] = None,
         metric_collection: Optional[str] = None,
         name: Optional[str] = None,
         tags: Optional[List[str]] = None,
         metadata: Optional[dict] = None,
         thread_id: Optional[str] = None,
         user_id: Optional[str] = None,
-        **kwargs, # backwards compatibility
+        **kwargs,  # backwards compatibility
     ) -> RunResultStreaming:
         is_agents_available()
         # Manually enter observer; we'll exit when streaming finishes
@@ -271,7 +274,7 @@ def run_streamed(
             previous_response_id=previous_response_id,
             conversation_id=conversation_id,
             session=session,
-            **kwargs, # backwards compatibility
+            **kwargs,  # backwards compatibility
         )
 
         # Runtime-patch stream_events so the observer closes only after streaming completes
@@ -290,10 +293,11 @@ async def _patched_stream_events(self: RunResultStreaming):
                 observer.__exit__(None, None, None)
 
         from types import MethodType as _MethodType
+
         res.stream_events = _MethodType(_patched_stream_events, res)
 
         return res
-        
+
 
 def update_trace_attributes(
     input: Any = None,
@@ -324,4 +328,4 @@ def update_trace_attributes(
     if metric_collection:
         current_trace.metric_collection = metric_collection
     if metrics:
-        current_trace.metrics = metrics
+        current_trace.metrics = metrics