remove use of tracer

Author: dot-agi

agentops/instrumentation/providers/openai/stream_wrapper.py

@@ -30,19 +30,17 @@ class OpenaiStreamWrapper:
     - Chunk statistics
     """
 
-    def __init__(self, stream: Any, span: Span, request_kwargs: dict, tracer=None):
+    def __init__(self, stream: Any, span: Span, request_kwargs: dict):
         """Initialize the stream wrapper.
 
         Args:
             stream: The original OpenAI stream object
             span: The OpenTelemetry span for tracking
             request_kwargs: Original request parameters for context
-            tracer: The OpenTelemetry tracer for creating child spans
         """
         self._stream = stream
         self._span = span
         self._request_kwargs = request_kwargs
-        self._tracer = tracer
         self._start_time = time.time()
         self._first_token_time = None
         self._chunk_count = 0
@@ -195,10 +193,10 @@ def _finalize_stream(self) -> None:
             self._span.set_attribute(MessageAttributes.COMPLETION_FINISH_REASON.format(i=0), self._finish_reason)
 
         # Create tool spans for each tool call
-        if len(self._tool_calls) > 0 and self._tracer is not None:
+        if len(self._tool_calls) > 0:
             for idx, tool_call in self._tool_calls.items():
                 # Create a child span for this tool call
-                _create_tool_span(self._span, tool_call, self._tracer)
+                _create_tool_span(self._span, tool_call)
 
         # Set usage if available from the API
         if self._usage is not None:
@@ -237,19 +235,17 @@ def _finalize_stream(self) -> None:
 class OpenAIAsyncStreamWrapper:
     """Async wrapper for OpenAI Chat Completions streaming responses."""
 
-    def __init__(self, stream: Any, span: Span, request_kwargs: dict, tracer=None):
+    def __init__(self, stream: Any, span: Span, request_kwargs: dict):
         """Initialize the async stream wrapper.
 
         Args:
             stream: The original OpenAI async stream object
             span: The OpenTelemetry span for tracking
             request_kwargs: Original request parameters for context
-            tracer: The OpenTelemetry tracer for creating child spans
         """
         self._stream = stream
         self._span = span
         self._request_kwargs = request_kwargs
-        self._tracer = tracer
         self._start_time = time.time()
         self._first_token_time = None
         self._chunk_count = 0
@@ -356,10 +352,10 @@ def chat_completion_stream_wrapper(tracer, wrapped, instance, args, kwargs):
         if is_streaming:
             # Wrap the stream
             context_api.detach(token)
-            return OpenaiStreamWrapper(response, span, kwargs, tracer)
+            return OpenaiStreamWrapper(response, span, kwargs)
         else:
             # Handle non-streaming response
-            response_attributes = handle_chat_attributes(kwargs=kwargs, return_value=response, span=span, tracer=tracer)
+            response_attributes = handle_chat_attributes(kwargs=kwargs, return_value=response, span=span)
 
             for key, value in response_attributes.items():
                 if key not in request_attributes:  # Avoid overwriting request attributes
@@ -421,10 +417,10 @@ async def async_chat_completion_stream_wrapper(tracer, wrapped, instance, args,
         if is_streaming:
             # Wrap the stream
             context_api.detach(token)
-            return OpenAIAsyncStreamWrapper(response, span, kwargs, tracer)
+            return OpenAIAsyncStreamWrapper(response, span, kwargs)
         else:
             # Handle non-streaming response
-            response_attributes = handle_chat_attributes(kwargs=kwargs, return_value=response, span=span, tracer=tracer)
+            response_attributes = handle_chat_attributes(kwargs=kwargs, return_value=response, span=span)
 
             for key, value in response_attributes.items():
                 if key not in request_attributes:  # Avoid overwriting request attributes

agentops/instrumentation/providers/openai/wrappers/chat.py

@@ -21,22 +21,24 @@
 from agentops.semconv.span_kinds import AgentOpsSpanKindValues
 
 from opentelemetry import context as context_api
-from opentelemetry.trace import SpanKind, Status, StatusCode
+from opentelemetry.trace import SpanKind, Status, StatusCode, get_tracer
 
 logger = logging.getLogger(__name__)
 
 LLM_REQUEST_TYPE = LLMRequestTypeValues.CHAT
 
 
-def _create_tool_span(parent_span, tool_call_data, tracer):
+def _create_tool_span(parent_span, tool_call_data):
     """
     Create a distinct span for each tool call.
     
     Args:
         parent_span: The parent LLM span
         tool_call_data: The tool call data dictionary
-        tracer: The OpenTelemetry tracer instance
     """
+    # Get the tracer for this module
+    tracer = get_tracer(__name__)
+    
     # Create a child span for the tool call
     with tracer.start_as_current_span(
         name=f"tool_call.{tool_call_data['function']['name']}",
@@ -61,7 +63,6 @@ def handle_chat_attributes(
     kwargs: Optional[Dict] = None,
     return_value: Optional[Any] = None,
     span: Optional[Span] = None,
-    tracer: Optional[Any] = None,
 ) -> AttributeMap:
     """Extract attributes from chat completion calls.
 
@@ -73,7 +74,6 @@ def handle_chat_attributes(
         kwargs: Method keyword arguments
         return_value: Method return value
         span: The parent span for creating tool spans
-        tracer: The OpenTelemetry tracer for creating child spans
     """
     attributes = {
         SpanAttributes.LLM_REQUEST_TYPE: LLM_REQUEST_TYPE.value,
@@ -235,7 +235,7 @@ def handle_chat_attributes(
                     # Tool calls
                     if "tool_calls" in message:
                         tool_calls = message["tool_calls"]
-                        if tool_calls and span is not None and tracer is not None:
+                        if tool_calls and span is not None:
                             for i, tool_call in enumerate(tool_calls):
                                 # Convert tool_call to the format expected by _create_tool_span
                                 function = tool_call.get("function", {})
@@ -248,7 +248,7 @@ def handle_chat_attributes(
                                     }
                                 }
                                 # Create a child span for this tool call
-                                _create_tool_span(span, tool_call_data, tracer)
+                                _create_tool_span(span, tool_call_data)
 
         # Prompt filter results
         if "prompt_filter_results" in response_dict: