Format code with Black to fix CI linting failures

All tests passing but CI was failing on Black format check.
Formatted 5 files to comply with Black style:
- examples/send_to_last9.py
- tests/test_context.py
- tests/test_decorators.py
- tests/test_e2e.py
- tests/test_core.py

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

Author: prathamesh-sonpatki

examples/send_to_last9.py

@@ -30,6 +30,7 @@
 # Import OTLP exporter
 try:
     from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
+
     OTLP_AVAILABLE = True
 except ImportError:
     print("❌ OTLP exporter not installed!")
@@ -74,11 +75,13 @@ def setup_last9_export():
     print(f"   Authentication: {'✅ Configured' if api_key else '❌ Missing'}")
 
     # Create resource with service identification
-    resource = Resource.create({
-        SERVICE_NAME: "last9-genai-python-example",
-        SERVICE_VERSION: "1.0.0",
-        "deployment.environment": os.getenv("ENVIRONMENT", "development"),
-    })
+    resource = Resource.create(
+        {
+            SERVICE_NAME: "last9-genai-python-example",
+            SERVICE_VERSION: "1.0.0",
+            "deployment.environment": os.getenv("ENVIRONMENT", "development"),
+        }
+    )
 
     # Create tracer provider with resource
     provider = TracerProvider(resource=resource)
@@ -231,10 +234,7 @@ def check_account():
 
             # LLM call
             print("   🤖 Generating support response...")
-            response = chat_with_llm(
-                "gpt-4o",
-                f"Generate support response for {account_info}"
-            )
+            response = chat_with_llm("gpt-4o", f"Generate support response for {account_info}")
 
             print(f"\n🤖 Support response: {response['content'][:80]}...\n")
 
@@ -283,6 +283,7 @@ def main():
     except Exception as e:
         print(f"\n❌ Error: {e}")
         import traceback
+
         traceback.print_exc()
         sys.exit(1)
 

tests/test_context.py

@@ -353,10 +353,9 @@ def thread_function(thread_id: int):
             with conversation_context(conversation_id=f"conv_{thread_id}"):
                 with tracer.start_as_current_span(f"span_{thread_id}"):
                     context = get_current_context()
-                    results.append({
-                        "thread_id": thread_id,
-                        "conversation_id": context.get("conversation_id")
-                    })
+                    results.append(
+                        {"thread_id": thread_id, "conversation_id": context.get("conversation_id")}
+                    )
 
         # Create multiple threads with different contexts
         threads = []

tests/test_core.py

@@ -655,9 +655,10 @@ def test_add_tool_call_events(self, tracer_setup):
         call_event = events[0]
         assert call_event.name == EventNames.GEN_AI_TOOL_CALL
         assert call_event.attributes[GenAIAttributes.TOOL_NAME] == "search"
-        assert '"query": "python tutorial"' in call_event.attributes[
-            Last9Attributes.FUNCTION_CALL_ARGUMENTS
-        ]
+        assert (
+            '"query": "python tutorial"'
+            in call_event.attributes[Last9Attributes.FUNCTION_CALL_ARGUMENTS]
+        )
 
         # Check tool result event
         result_event = events[1]
@@ -737,9 +738,7 @@ def test_create_tool_span_with_workflow(self, tracer_setup):
         """Test creating tool span with workflow"""
         tracer, memory_exporter = tracer_setup
 
-        span = create_tool_span(
-            tracer, "api_call", tool_type="external_api", workflow_id="wf_tool"
-        )
+        span = create_tool_span(tracer, "api_call", tool_type="external_api", workflow_id="wf_tool")
         span.end()
 
         spans = memory_exporter.get_finished_spans()

tests/test_decorators.py

@@ -12,32 +12,52 @@
 
 class MockOpenAIResponse:
     """Mock OpenAI API response"""
-    def __init__(self, response_id="chatcmpl-123", model="gpt-4o-mini", content="Hello!", input_tokens=10, output_tokens=20):
+
+    def __init__(
+        self,
+        response_id="chatcmpl-123",
+        model="gpt-4o-mini",
+        content="Hello!",
+        input_tokens=10,
+        output_tokens=20,
+    ):
         self.id = response_id
         self.model = model
         self.choices = [
-            type('Choice', (), {
-                'message': type('Message', (), {'content': content})(),
-                'finish_reason': 'stop'
-            })()
+            type(
+                "Choice",
+                (),
+                {"message": type("Message", (), {"content": content})(), "finish_reason": "stop"},
+            )()
         ]
-        self.usage = type('Usage', (), {
-            'prompt_tokens': input_tokens,
-            'completion_tokens': output_tokens,
-            'total_tokens': input_tokens + output_tokens
-        })()
+        self.usage = type(
+            "Usage",
+            (),
+            {
+                "prompt_tokens": input_tokens,
+                "completion_tokens": output_tokens,
+                "total_tokens": input_tokens + output_tokens,
+            },
+        )()
 
 
 class MockAnthropicResponse:
     """Mock Anthropic API response"""
-    def __init__(self, response_id="msg_123", model="claude-3-haiku", content="Hello!", input_tokens=10, output_tokens=20):
+
+    def __init__(
+        self,
+        response_id="msg_123",
+        model="claude-3-haiku",
+        content="Hello!",
+        input_tokens=10,
+        output_tokens=20,
+    ):
         self.id = response_id
         self.model = model
-        self.content = [type('Content', (), {'text': content})()]
-        self.usage = type('Usage', (), {
-            'input_tokens': input_tokens,
-            'output_tokens': output_tokens
-        })()
+        self.content = [type("Content", (), {"text": content})()]
+        self.usage = type(
+            "Usage", (), {"input_tokens": input_tokens, "output_tokens": output_tokens}
+        )()
 
 
 class TestObserveDecorator:
@@ -176,7 +196,7 @@ def call_openai(prompt: str):
                 model="gpt-4o-mini",
                 content="Hello from OpenAI!",
                 input_tokens=10,
-                output_tokens=25
+                output_tokens=25,
             )
 
         result = call_openai("Say hello")
@@ -209,7 +229,7 @@ def call_anthropic(prompt: str):
                 model="claude-3-haiku",
                 content="Hello from Claude!",
                 input_tokens=15,
-                output_tokens=30
+                output_tokens=30,
             )
 
         result = call_anthropic("Say hello")
@@ -375,11 +395,7 @@ def test_decorator_cost_calculation(self, tracer_setup):
 
         @observe(pricing=pricing)
         def expensive_call():
-            return MockOpenAIResponse(
-                model="gpt-4o",
-                input_tokens=1000,
-                output_tokens=500
-            )
+            return MockOpenAIResponse(model="gpt-4o", input_tokens=1000, output_tokens=500)
 
         expensive_call()
 
@@ -443,10 +459,7 @@ async def test_async_decorator_with_llm_response(self, tracer_setup):
         @observe(pricing=pricing, tags=["async", "test"])
         async def async_llm_call(prompt: str):
             # Simulate async API call
-            return MockOpenAIResponse(
-                response_id="chatcmpl-async123",
-                content="Async response"
-            )
+            return MockOpenAIResponse(response_id="chatcmpl-async123", content="Async response")
 
         result = await async_llm_call("test prompt")
         assert result.id == "chatcmpl-async123"