Add Support for Async openai instrumentation (#2984)

.github/component_owners.yml

@@ -73,4 +73,5 @@ components:
     - lzchen
     - gyliu513
     - nirga
+    - alizenhom
     - codefromthecrypt

instrumentation-genai/opentelemetry-instrumentation-openai-v2/CHANGELOG.md

@@ -7,6 +7,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## Unreleased
 
+- Support for `AsyncOpenAI/AsyncCompletions` ([#2984](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/2984))
+
 ## Version 2.0b0 (2024-11-08)
 
 - Use generic `OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT` environment variable

instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/__init__.py

@@ -52,7 +52,7 @@ from opentelemetry.instrumentation.utils import unwrap
 from opentelemetry.semconv.schemas import Schemas
 from opentelemetry.trace import get_tracer
 
-from .patch import chat_completions_create
+from .patch import async_chat_completions_create, chat_completions_create
 
 
 class OpenAIInstrumentor(BaseInstrumentor):
@@ -84,7 +84,16 @@ class OpenAIInstrumentor(BaseInstrumentor):
             ),
         )
 
+        wrap_function_wrapper(
+            module="openai.resources.chat.completions",
+            name="AsyncCompletions.create",
+            wrapper=async_chat_completions_create(
+                tracer, event_logger, is_content_enabled()
+            ),
+        )
+
     def _uninstrument(self, **kwargs):
         import openai  # pylint: disable=import-outside-toplevel
 
         unwrap(openai.resources.chat.completions.Completions, "create")
+        unwrap(openai.resources.chat.completions.AsyncCompletions, "create")

instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/patch.py

@@ -21,15 +21,12 @@ from opentelemetry._events import Event, EventLogger
 from opentelemetry.semconv._incubating.attributes import (
     gen_ai_attributes as GenAIAttributes,
 )
-from opentelemetry.semconv.attributes import (
-    error_attributes as ErrorAttributes,
-)
 from opentelemetry.trace import Span, SpanKind, Tracer
-from opentelemetry.trace.status import Status, StatusCode
 
 from .utils import (
     choice_to_event,
     get_llm_request_attributes,
+    handle_span_exception,
     is_streaming,
     message_to_event,
     set_span_attribute,
@@ -72,12 +69,49 @@ def chat_completions_create(
                 return result
 
             except Exception as error:
-                span.set_status(Status(StatusCode.ERROR, str(error)))
+                handle_span_exception(span, error)
+                raise
+
+    return traced_method
+
+
+def async_chat_completions_create(
+    tracer: Tracer, event_logger: EventLogger, capture_content: bool
+):
+    """Wrap the `create` method of the `AsyncChatCompletion` class to trace it."""
+
+    async def traced_method(wrapped, instance, args, kwargs):
+        span_attributes = {**get_llm_request_attributes(kwargs, instance)}
+
+        span_name = f"{span_attributes[GenAIAttributes.GEN_AI_OPERATION_NAME]} {span_attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL]}"
+        with tracer.start_as_current_span(
+            name=span_name,
+            kind=SpanKind.CLIENT,
+            attributes=span_attributes,
+            end_on_exit=False,
+        ) as span:
+            if span.is_recording():
+                for message in kwargs.get("messages", []):
+                    event_logger.emit(
+                        message_to_event(message, capture_content)
+                    )
+
+            try:
+                result = await wrapped(*args, **kwargs)
+                if is_streaming(kwargs):
+                    return StreamWrapper(
+                        result, span, event_logger, capture_content
+                    )
+
                 if span.is_recording():
-                    span.set_attribute(
-                        ErrorAttributes.ERROR_TYPE, type(error).__qualname__
+                    _set_response_attributes(
+                        span, result, event_logger, capture_content
                     )
                 span.end()
+                return result
+
+            except Exception as error:
+                handle_span_exception(span, error)
                 raise
 
     return traced_method
@@ -286,10 +320,19 @@ class StreamWrapper:
     def __exit__(self, exc_type, exc_val, exc_tb):
         try:
             if exc_type is not None:
-                self.span.set_status(Status(StatusCode.ERROR, str(exc_val)))
-                self.span.set_attribute(
-                    ErrorAttributes.ERROR_TYPE, exc_type.__qualname__
-                )
+                handle_span_exception(self.span, exc_val)
+        finally:
+            self.cleanup()
+        return False  # Propagate the exception
+
+    async def __aenter__(self):
+        self.setup()
+        return self
+
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        try:
+            if exc_type is not None:
+                handle_span_exception(self.span, exc_val)
         finally:
             self.cleanup()
         return False  # Propagate the exception
@@ -301,6 +344,9 @@ class StreamWrapper:
     def __iter__(self):
         return self
 
+    def __aiter__(self):
+        return self
+
     def __next__(self):
         try:
             chunk = next(self.stream)
@@ -310,10 +356,20 @@ class StreamWrapper:
             self.cleanup()
             raise
         except Exception as error:
-            self.span.set_status(Status(StatusCode.ERROR, str(error)))
-            self.span.set_attribute(
-                ErrorAttributes.ERROR_TYPE, type(error).__qualname__
-            )
+            handle_span_exception(self.span, error)
+            self.cleanup()
+            raise
+
+    async def __anext__(self):
+        try:
+            chunk = await self.stream.__anext__()
+            self.process_chunk(chunk)
+            return chunk
+        except StopAsyncIteration:
+            self.cleanup()
+            raise
+        except Exception as error:
+            handle_span_exception(self.span, error)
             self.cleanup()
             raise
 

instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/utils.py

@@ -26,6 +26,10 @@ from opentelemetry.semconv._incubating.attributes import (
 from opentelemetry.semconv._incubating.attributes import (
     server_attributes as ServerAttributes,
 )
+from opentelemetry.semconv.attributes import (
+    error_attributes as ErrorAttributes,
+)
+from opentelemetry.trace.status import Status, StatusCode
 
 OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT = (
     "OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT"
@@ -138,9 +142,11 @@ def choice_to_event(choice, capture_content):
 
     if choice.message:
         message = {
-            "role": choice.message.role
-            if choice.message and choice.message.role
-            else None
+            "role": (
+                choice.message.role
+                if choice.message and choice.message.role
+                else None
+            )
         }
         tool_calls = extract_tool_calls(choice.message, capture_content)
         if tool_calls:
@@ -210,3 +216,12 @@ def get_llm_request_attributes(
 
     # filter out None values
     return {k: v for k, v in attributes.items() if v is not None}
+
+
+def handle_span_exception(span, error):
+    span.set_status(Status(StatusCode.ERROR, str(error)))
+    if span.is_recording():
+        span.set_attribute(
+            ErrorAttributes.ERROR_TYPE, type(error).__qualname__
+        )
+    span.end()

instrumentation-genai/opentelemetry-instrumentation-openai-v2/test-requirements-0.txt

@@ -5,6 +5,7 @@ importlib-metadata==6.11.0
 packaging==24.0
 pytest==7.4.4
 pytest-vcr==1.0.2
+pytest-asyncio==0.21.0
 wrapt==1.16.0
 opentelemetry-api==1.28  # when updating, also update in pyproject.toml
 opentelemetry-sdk==1.28  # when updating, also update in pyproject.toml

instrumentation-genai/opentelemetry-instrumentation-openai-v2/test-requirements-1.txt

@@ -5,6 +5,7 @@ importlib-metadata==6.11.0
 packaging==24.0
 pytest==7.4.4
 pytest-vcr==1.0.2
+pytest-asyncio==0.21.0
 wrapt==1.16.0
 # test with the latest version of opentelemetry-api, sdk, and semantic conventions
 

instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/cassettes/test_async_chat_completion_404.yaml

@@ -0,0 +1,89 @@
+interactions:
+- request:
+    body: |-
+      {
+        "messages": [
+          {
+            "role": "user",
+            "content": "Say this is a test"
+          }
+        ],
+        "model": "this-model-does-not-exist"
+      }
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      authorization:
+      - Bearer test_openai_api_key
+      connection:
+      - keep-alive
+      content-length:
+      - '103'
+      content-type:
+      - application/json
+      host:
+      - api.openai.com
+      user-agent:
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+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - async:asyncio
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.26.0
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.5
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    body:
+      string: |-
+        {
+          "error": {
+            "message": "The model `this-model-does-not-exist` does not exist or you do not have access to it.",
+            "type": "invalid_request_error",
+            "param": null,
+            "code": "model_not_found"
+          }
+        }
+    headers:
+      CF-Cache-Status:
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+      Set-Cookie: test_set_cookie
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+    status:
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+      message: Not Found
+version: 1

instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/cassettes/test_async_chat_completion_extra_params.yaml

@@ -0,0 +1,137 @@
+interactions:
+- request:
+    body: |-
+      {
+        "messages": [
+          {
+            "role": "user",
+            "content": "Say this is a test"
+          }
+        ],
+        "model": "gpt-4o-mini",
+        "max_tokens": 50,
+        "seed": 42,
+        "stream": false,
+        "temperature": 0.5,
+        "service_tier": "default"
+      }
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+      x-stainless-runtime-version:
+      - 3.12.5
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
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+      string: |-
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+          "id": "chatcmpl-ASv9WMTAMZY4O1EImv3csZa6Ch7KI",
+          "object": "chat.completion",
+          "created": 1731456242,
+          "model": "gpt-4o-mini-2024-07-18",
+          "choices": [
+            {
+              "index": 0,
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+                "content": "This is a test. How can I assist you further?",
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+              },
+              "logprobs": null,
+              "finish_reason": "stop"
+            }
+          ],
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+            "prompt_tokens": 12,
+            "completion_tokens": 12,
+            "total_tokens": 24,
+            "prompt_tokens_details": {
+              "cached_tokens": 0,
+              "audio_tokens": 0
+            },
+            "completion_tokens_details": {
+              "reasoning_tokens": 0,
+              "audio_tokens": 0,
+              "accepted_prediction_tokens": 0,
+              "rejected_prediction_tokens": 0
+            }
+          },
+          "service_tier": "default",
+          "system_fingerprint": "fp_0ba0d124f1"
+        }
+    headers:
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instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/cassettes/test_async_chat_completion_multiple_choices.yaml

@@ -0,0 +1,143 @@
+interactions:
+- request:
+    body: |-
+      {
+        "messages": [
+          {
+            "role": "user",
+            "content": "Say this is a test"
+          }
+        ],
+        "model": "gpt-4o-mini",
+        "n": 2,
+        "stream": false
+      }
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      authorization:
+      - Bearer test_openai_api_key
+      connection:
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+      - '114'
+      content-type:
+      - application/json
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+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.5
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    body:
+      string: |-
+        {
+          "id": "chatcmpl-ASv9XLlMmT7H3cf50dNTesHDBDwX5",
+          "object": "chat.completion",
+          "created": 1731456243,
+          "model": "gpt-4o-mini-2024-07-18",
+          "choices": [
+            {
+              "index": 0,
+              "message": {
+                "role": "assistant",
+                "content": "This is a test.",
+                "refusal": null
+              },
+              "logprobs": null,
+              "finish_reason": "stop"
+            },
+            {
+              "index": 1,
+              "message": {
+                "role": "assistant",
+                "content": "This is a test.",
+                "refusal": null
+              },
+              "logprobs": null,
+              "finish_reason": "stop"
+            }
+          ],
+          "usage": {
+            "prompt_tokens": 12,
+            "completion_tokens": 10,
+            "total_tokens": 22,
+            "prompt_tokens_details": {
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+              "audio_tokens": 0
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instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/cassettes/test_async_chat_completion_multiple_choices_streaming.yaml

@@ -0,0 +1,382 @@
+interactions:
+- request:
+    body: |-
+      {
+        "messages": [
+          {
+            "role": "system",
+            "content": "You're a helpful assistant."
+          },
+          {
+            "role": "user",
+            "content": "What's the weather in Seattle and San Francisco today?"
+          }
+        ],
+        "model": "gpt-4o-mini",
+        "n": 2,
+        "stream": true,
+        "stream_options": {
+          "include_usage": true
+        }
+      }
+    headers:
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+      accept-encoding:
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+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
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+
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+
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+
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+
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+
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+
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+
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instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/cassettes/test_async_chat_completion_multiple_tools_streaming_no_content.yaml

@@ -0,0 +1,164 @@
+interactions:
+- request:
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+            "role": "system",
+            "content": "You're a helpful assistant."
+          },
+          {
+            "role": "user",
+            "content": "What's the weather in Seattle and San Francisco today?"
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+                },
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+              }
+            ]
+          },
+          {
+            "role": "tool",
+            "content": "50 degrees and raining",
+            "tool_call_id": "call_O8NOz8VlxosSASEsOY7LDUcP"
+          },
+          {
+            "role": "tool",
+            "content": "70 degrees and sunny",
+            "tool_call_id": "call_3m7cyuckijnpiWr6tq0Tl8Mg"
+          }
+        ],
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+      }
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+          "choices": [
+            {
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+              "message": {
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+                "content": "Today, Seattle is experiencing 50 degrees and raining, while San Francisco has a pleasant 70 degrees and sunny weather.",
+                "refusal": null
+              },
+              "logprobs": null,
+              "finish_reason": "stop"
+            }
+          ],
+          "usage": {
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+            "completion_tokens": 24,
+            "total_tokens": 123,
+            "prompt_tokens_details": {
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+            },
+            "completion_tokens_details": {
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+            }
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+        }
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instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/cassettes/test_async_chat_completion_with_content.yaml

@@ -0,0 +1,132 @@
+interactions:
+- request:
+    body: |-
+      {
+        "messages": [
+          {
+            "role": "user",
+            "content": "Say this is a test"
+          }
+        ],
+        "model": "gpt-4o-mini",
+        "stream": false
+      }
+    headers:
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+      content-type:
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+      x-stainless-os:
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+      x-stainless-package-version:
+      - 1.26.0
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.5
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    body:
+      string: |-
+        {
+          "id": "chatcmpl-ASv9R2E7Yhb2e7bj4Xl0qm9s3J42Y",
+          "object": "chat.completion",
+          "created": 1731456237,
+          "model": "gpt-4o-mini-2024-07-18",
+          "choices": [
+            {
+              "index": 0,
+              "message": {
+                "role": "assistant",
+                "content": "This is a test. How can I assist you further?",
+                "refusal": null
+              },
+              "logprobs": null,
+              "finish_reason": "stop"
+            }
+          ],
+          "usage": {
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+            "completion_tokens": 12,
+            "total_tokens": 24,
+            "prompt_tokens_details": {
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+            "completion_tokens_details": {
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+              "audio_tokens": 0,
+              "accepted_prediction_tokens": 0,
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+      - req_41ea134c1fc450d4ca4cf8d0c6a7c53a
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+version: 1

instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/conftest.py

@@ -5,7 +5,7 @@ import os
 
 import pytest
 import yaml
-from openai import OpenAI
+from openai import AsyncOpenAI, OpenAI
 
 from opentelemetry.instrumentation.openai_v2 import OpenAIInstrumentor
 from opentelemetry.instrumentation.openai_v2.utils import (
@@ -63,6 +63,11 @@ def openai_client():
     return OpenAI()
 
 
+@pytest.fixture
+def async_openai_client():
+    return AsyncOpenAI()
+
+
 @pytest.fixture(scope="module")
 def vcr_config():
     return {

instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/test_async_chat_completions.py

@@ -0,0 +1,847 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# pylint: disable=too-many-locals
+
+from typing import Optional
+
+import pytest
+from openai import APIConnectionError, AsyncOpenAI, NotFoundError
+from openai.resources.chat.completions import ChatCompletion
+
+from opentelemetry.sdk.trace import ReadableSpan
+from opentelemetry.semconv._incubating.attributes import (
+    error_attributes as ErrorAttributes,
+)
+from opentelemetry.semconv._incubating.attributes import (
+    event_attributes as EventAttributes,
+)
+from opentelemetry.semconv._incubating.attributes import (
+    gen_ai_attributes as GenAIAttributes,
+)
+from opentelemetry.semconv._incubating.attributes import (
+    server_attributes as ServerAttributes,
+)
+
+
+@pytest.mark.vcr()
+@pytest.mark.asyncio()
+async def test_async_chat_completion_with_content(
+    span_exporter, log_exporter, async_openai_client, instrument_with_content
+):
+    llm_model_value = "gpt-4o-mini"
+    messages_value = [{"role": "user", "content": "Say this is a test"}]
+
+    response = await async_openai_client.chat.completions.create(
+        messages=messages_value, model=llm_model_value, stream=False
+    )
+
+    spans = span_exporter.get_finished_spans()
+    assert_completion_attributes(spans[0], llm_model_value, response)
+
+    logs = log_exporter.get_finished_logs()
+    assert len(logs) == 2
+
+    user_message = {"content": messages_value[0]["content"]}
+    assert_message_in_logs(
+        logs[0], "gen_ai.user.message", user_message, spans[0]
+    )
+
+    choice_event = {
+        "index": 0,
+        "finish_reason": "stop",
+        "message": {
+            "role": "assistant",
+            "content": response.choices[0].message.content,
+        },
+    }
+    assert_message_in_logs(logs[1], "gen_ai.choice", choice_event, spans[0])
+
+
+@pytest.mark.asyncio()
+async def test_async_chat_completion_bad_endpoint(
+    span_exporter, instrument_no_content
+):
+    llm_model_value = "gpt-4o-mini"
+    messages_value = [{"role": "user", "content": "Say this is a test"}]
+
+    client = AsyncOpenAI(base_url="http://localhost:4242")
+
+    with pytest.raises(APIConnectionError):
+        await client.chat.completions.create(
+            messages=messages_value,
+            model=llm_model_value,
+            timeout=0.1,
+        )
+
+    spans = span_exporter.get_finished_spans()
+    assert_all_attributes(
+        spans[0], llm_model_value, server_address="localhost"
+    )
+    assert 4242 == spans[0].attributes[ServerAttributes.SERVER_PORT]
+    assert (
+        "APIConnectionError" == spans[0].attributes[ErrorAttributes.ERROR_TYPE]
+    )
+
+
+@pytest.mark.vcr()
+@pytest.mark.asyncio()
+async def test_async_chat_completion_404(
+    span_exporter, async_openai_client, instrument_no_content
+):
+    llm_model_value = "this-model-does-not-exist"
+    messages_value = [{"role": "user", "content": "Say this is a test"}]
+
+    with pytest.raises(NotFoundError):
+        await async_openai_client.chat.completions.create(
+            messages=messages_value,
+            model=llm_model_value,
+        )
+
+    spans = span_exporter.get_finished_spans()
+
+    assert_all_attributes(spans[0], llm_model_value)
+    assert "NotFoundError" == spans[0].attributes[ErrorAttributes.ERROR_TYPE]
+
+
+@pytest.mark.vcr()
+@pytest.mark.asyncio()
+async def test_async_chat_completion_extra_params(
+    span_exporter, async_openai_client, instrument_no_content
+):
+    llm_model_value = "gpt-4o-mini"
+    messages_value = [{"role": "user", "content": "Say this is a test"}]
+
+    response = await async_openai_client.chat.completions.create(
+        messages=messages_value,
+        model=llm_model_value,
+        seed=42,
+        temperature=0.5,
+        max_tokens=50,
+        stream=False,
+        extra_body={"service_tier": "default"},
+    )
+
+    spans = span_exporter.get_finished_spans()
+    assert_completion_attributes(spans[0], llm_model_value, response)
+    assert (
+        spans[0].attributes[GenAIAttributes.GEN_AI_OPENAI_REQUEST_SEED] == 42
+    )
+    assert (
+        spans[0].attributes[GenAIAttributes.GEN_AI_REQUEST_TEMPERATURE] == 0.5
+    )
+    assert spans[0].attributes[GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS] == 50
+    assert (
+        spans[0].attributes[GenAIAttributes.GEN_AI_OPENAI_REQUEST_SERVICE_TIER]
+        == "default"
+    )
+
+
+@pytest.mark.vcr()
+@pytest.mark.asyncio()
+async def test_async_chat_completion_multiple_choices(
+    span_exporter, log_exporter, async_openai_client, instrument_with_content
+):
+    llm_model_value = "gpt-4o-mini"
+    messages_value = [{"role": "user", "content": "Say this is a test"}]
+
+    response = await async_openai_client.chat.completions.create(
+        messages=messages_value, model=llm_model_value, n=2, stream=False
+    )
+
+    spans = span_exporter.get_finished_spans()
+    assert_completion_attributes(spans[0], llm_model_value, response)
+
+    logs = log_exporter.get_finished_logs()
+    assert len(logs) == 3  # 1 user message + 2 choice messages
+
+    user_message = {"content": messages_value[0]["content"]}
+    assert_message_in_logs(
+        logs[0], "gen_ai.user.message", user_message, spans[0]
+    )
+
+    choice_event_0 = {
+        "index": 0,
+        "finish_reason": "stop",
+        "message": {
+            "role": "assistant",
+            "content": response.choices[0].message.content,
+        },
+    }
+    assert_message_in_logs(logs[1], "gen_ai.choice", choice_event_0, spans[0])
+
+    choice_event_1 = {
+        "index": 1,
+        "finish_reason": "stop",
+        "message": {
+            "role": "assistant",
+            "content": response.choices[1].message.content,
+        },
+    }
+    assert_message_in_logs(logs[2], "gen_ai.choice", choice_event_1, spans[0])
+
+
+@pytest.mark.vcr()
+@pytest.mark.asyncio()
+async def test_async_chat_completion_tool_calls_with_content(
+    span_exporter, log_exporter, async_openai_client, instrument_with_content
+):
+    await chat_completion_tool_call(
+        span_exporter, log_exporter, async_openai_client, True
+    )
+
+
+@pytest.mark.vcr()
+@pytest.mark.asyncio()
+async def test_async_chat_completion_tool_calls_no_content(
+    span_exporter, log_exporter, async_openai_client, instrument_no_content
+):
+    await chat_completion_tool_call(
+        span_exporter, log_exporter, async_openai_client, False
+    )
+
+
+async def chat_completion_tool_call(
+    span_exporter, log_exporter, async_openai_client, expect_content
+):
+    llm_model_value = "gpt-4o-mini"
+    messages_value = [
+        {"role": "system", "content": "You're a helpful assistant."},
+        {
+            "role": "user",
+            "content": "What's the weather in Seattle and San Francisco today?",
+        },
+    ]
+
+    response_0 = await async_openai_client.chat.completions.create(
+        messages=messages_value,
+        model=llm_model_value,
+        tool_choice="auto",
+        tools=[get_current_weather_tool_definition()],
+    )
+
+    # sanity check
+    assert "tool_calls" in response_0.choices[0].finish_reason
+
+    # final request
+    messages_value.append(
+        {
+            "role": "assistant",
+            "tool_calls": response_0.choices[0].message.to_dict()[
+                "tool_calls"
+            ],
+        }
+    )
+
+    tool_call_result_0 = {
+        "role": "tool",
+        "content": "50 degrees and raining",
+        "tool_call_id": response_0.choices[0].message.tool_calls[0].id,
+    }
+    tool_call_result_1 = {
+        "role": "tool",
+        "content": "70 degrees and sunny",
+        "tool_call_id": response_0.choices[0].message.tool_calls[1].id,
+    }
+
+    messages_value.append(tool_call_result_0)
+    messages_value.append(tool_call_result_1)
+
+    response_1 = await async_openai_client.chat.completions.create(
+        messages=messages_value, model=llm_model_value
+    )
+
+    # sanity check
+    assert "stop" in response_1.choices[0].finish_reason
+
+    # validate both calls
+    spans = span_exporter.get_finished_spans()
+    assert len(spans) == 2
+    assert_completion_attributes(spans[0], llm_model_value, response_0)
+    assert_completion_attributes(spans[1], llm_model_value, response_1)
+
+    logs = log_exporter.get_finished_logs()
+    assert len(logs) == 9  # 3 logs for first completion, 6 for second
+
+    # call one
+    system_message = (
+        {"content": messages_value[0]["content"]} if expect_content else None
+    )
+    assert_message_in_logs(
+        logs[0], "gen_ai.system.message", system_message, spans[0]
+    )
+
+    user_message = (
+        {"content": messages_value[1]["content"]} if expect_content else None
+    )
+    assert_message_in_logs(
+        logs[1], "gen_ai.user.message", user_message, spans[0]
+    )
+
+    function_call_0 = {"name": "get_current_weather"}
+    function_call_1 = {"name": "get_current_weather"}
+    if expect_content:
+        function_call_0["arguments"] = (
+            response_0.choices[0]
+            .message.tool_calls[0]
+            .function.arguments.replace("\n", "")
+        )
+        function_call_1["arguments"] = (
+            response_0.choices[0]
+            .message.tool_calls[1]
+            .function.arguments.replace("\n", "")
+        )
+
+    choice_event = {
+        "index": 0,
+        "finish_reason": "tool_calls",
+        "message": {
+            "role": "assistant",
+            "tool_calls": [
+                {
+                    "id": response_0.choices[0].message.tool_calls[0].id,
+                    "type": "function",
+                    "function": function_call_0,
+                },
+                {
+                    "id": response_0.choices[0].message.tool_calls[1].id,
+                    "type": "function",
+                    "function": function_call_1,
+                },
+            ],
+        },
+    }
+    assert_message_in_logs(logs[2], "gen_ai.choice", choice_event, spans[0])
+
+    # call two
+    system_message = (
+        {"content": messages_value[0]["content"]} if expect_content else None
+    )
+    assert_message_in_logs(
+        logs[3], "gen_ai.system.message", system_message, spans[1]
+    )
+
+    user_message = (
+        {"content": messages_value[1]["content"]} if expect_content else None
+    )
+    assert_message_in_logs(
+        logs[4], "gen_ai.user.message", user_message, spans[1]
+    )
+
+    assistant_tool_call = {"tool_calls": messages_value[2]["tool_calls"]}
+    if not expect_content:
+        assistant_tool_call["tool_calls"][0]["function"]["arguments"] = None
+        assistant_tool_call["tool_calls"][1]["function"]["arguments"] = None
+
+    assert_message_in_logs(
+        logs[5], "gen_ai.assistant.message", assistant_tool_call, spans[1]
+    )
+
+    tool_message_0 = {
+        "id": tool_call_result_0["tool_call_id"],
+        "content": tool_call_result_0["content"] if expect_content else None,
+    }
+
+    assert_message_in_logs(
+        logs[6], "gen_ai.tool.message", tool_message_0, spans[1]
+    )
+
+    tool_message_1 = {
+        "id": tool_call_result_1["tool_call_id"],
+        "content": tool_call_result_1["content"] if expect_content else None,
+    }
+
+    assert_message_in_logs(
+        logs[7], "gen_ai.tool.message", tool_message_1, spans[1]
+    )
+
+    message = {
+        "role": "assistant",
+        "content": response_1.choices[0].message.content
+        if expect_content
+        else None,
+    }
+    choice = {
+        "index": 0,
+        "finish_reason": "stop",
+        "message": message,
+    }
+    assert_message_in_logs(logs[8], "gen_ai.choice", choice, spans[1])
+
+
+@pytest.mark.vcr()
+@pytest.mark.asyncio()
+async def test_async_chat_completion_streaming(
+    span_exporter, log_exporter, async_openai_client, instrument_with_content
+):
+    llm_model_value = "gpt-4"
+    messages_value = [{"role": "user", "content": "Say this is a test"}]
+
+    kwargs = {
+        "model": llm_model_value,
+        "messages": messages_value,
+        "stream": True,
+        "stream_options": {"include_usage": True},
+    }
+
+    response_stream_usage = None
+    response_stream_model = None
+    response_stream_id = None
+    response_stream_result = ""
+    response = await async_openai_client.chat.completions.create(**kwargs)
+    async for chunk in response:
+        if chunk.choices:
+            response_stream_result += chunk.choices[0].delta.content or ""
+
+        # get the last chunk
+        if getattr(chunk, "usage", None):
+            response_stream_usage = chunk.usage
+            response_stream_model = chunk.model
+            response_stream_id = chunk.id
+
+    spans = span_exporter.get_finished_spans()
+    assert_all_attributes(
+        spans[0],
+        llm_model_value,
+        response_stream_id,
+        response_stream_model,
+        response_stream_usage.prompt_tokens,
+        response_stream_usage.completion_tokens,
+    )
+
+    logs = log_exporter.get_finished_logs()
+    assert len(logs) == 2
+
+    user_message = {"content": "Say this is a test"}
+    assert_message_in_logs(
+        logs[0], "gen_ai.user.message", user_message, spans[0]
+    )
+
+    choice_event = {
+        "index": 0,
+        "finish_reason": "stop",
+        "message": {"role": "assistant", "content": response_stream_result},
+    }
+    assert_message_in_logs(logs[1], "gen_ai.choice", choice_event, spans[0])
+
+
+@pytest.mark.vcr()
+@pytest.mark.asyncio()
+async def test_async_chat_completion_streaming_not_complete(
+    span_exporter, log_exporter, async_openai_client, instrument_with_content
+):
+    llm_model_value = "gpt-4"
+    messages_value = [{"role": "user", "content": "Say this is a test"}]
+
+    kwargs = {
+        "model": llm_model_value,
+        "messages": messages_value,
+        "stream": True,
+    }
+
+    response_stream_model = None
+    response_stream_id = None
+    response_stream_result = ""
+    response = await async_openai_client.chat.completions.create(**kwargs)
+    idx = 0
+    async for chunk in response:
+        if chunk.choices:
+            response_stream_result += chunk.choices[0].delta.content or ""
+        if idx == 1:
+            # fake a stop
+            break
+
+        if chunk.model:
+            response_stream_model = chunk.model
+        if chunk.id:
+            response_stream_id = chunk.id
+        idx += 1
+
+    response.close()
+    spans = span_exporter.get_finished_spans()
+    assert_all_attributes(
+        spans[0], llm_model_value, response_stream_id, response_stream_model
+    )
+
+    logs = log_exporter.get_finished_logs()
+    assert len(logs) == 2
+
+    user_message = {"content": "Say this is a test"}
+    assert_message_in_logs(
+        logs[0], "gen_ai.user.message", user_message, spans[0]
+    )
+
+    choice_event = {
+        "index": 0,
+        "finish_reason": "error",
+        "message": {"role": "assistant", "content": response_stream_result},
+    }
+    assert_message_in_logs(logs[1], "gen_ai.choice", choice_event, spans[0])
+
+
+@pytest.mark.vcr()
+@pytest.mark.asyncio()
+async def test_async_chat_completion_multiple_choices_streaming(
+    span_exporter, log_exporter, async_openai_client, instrument_with_content
+):
+    llm_model_value = "gpt-4o-mini"
+    messages_value = [
+        {"role": "system", "content": "You're a helpful assistant."},
+        {
+            "role": "user",
+            "content": "What's the weather in Seattle and San Francisco today?",
+        },
+    ]
+
+    response_0 = await async_openai_client.chat.completions.create(
+        messages=messages_value,
+        model=llm_model_value,
+        n=2,
+        stream=True,
+        stream_options={"include_usage": True},
+    )
+
+    # two strings for each choice
+    response_stream_result = ["", ""]
+    finish_reasons = ["", ""]
+    async for chunk in response_0:
+        if chunk.choices:
+            for choice in chunk.choices:
+                response_stream_result[choice.index] += (
+                    choice.delta.content or ""
+                )
+                if choice.finish_reason:
+                    finish_reasons[choice.index] = choice.finish_reason
+
+        # get the last chunk
+        if getattr(chunk, "usage", None):
+            response_stream_usage = chunk.usage
+            response_stream_model = chunk.model
+            response_stream_id = chunk.id
+
+    # sanity check
+    assert "stop" == finish_reasons[0]
+
+    spans = span_exporter.get_finished_spans()
+    assert_all_attributes(
+        spans[0],
+        llm_model_value,
+        response_stream_id,
+        response_stream_model,
+        response_stream_usage.prompt_tokens,
+        response_stream_usage.completion_tokens,
+    )
+
+    logs = log_exporter.get_finished_logs()
+    assert len(logs) == 4
+
+    system_message = {"content": messages_value[0]["content"]}
+    assert_message_in_logs(
+        logs[0], "gen_ai.system.message", system_message, spans[0]
+    )
+
+    user_message = {
+        "content": "What's the weather in Seattle and San Francisco today?"
+    }
+    assert_message_in_logs(
+        logs[1], "gen_ai.user.message", user_message, spans[0]
+    )
+
+    choice_event_0 = {
+        "index": 0,
+        "finish_reason": "stop",
+        "message": {
+            "role": "assistant",
+            "content": "".join(response_stream_result[0]),
+        },
+    }
+    assert_message_in_logs(logs[2], "gen_ai.choice", choice_event_0, spans[0])
+
+    choice_event_1 = {
+        "index": 1,
+        "finish_reason": "stop",
+        "message": {
+            "role": "assistant",
+            "content": "".join(response_stream_result[1]),
+        },
+    }
+    assert_message_in_logs(logs[3], "gen_ai.choice", choice_event_1, spans[0])
+
+
+@pytest.mark.vcr()
+@pytest.mark.asyncio()
+async def test_async_chat_completion_multiple_tools_streaming_with_content(
+    span_exporter, log_exporter, async_openai_client, instrument_with_content
+):
+    await async_chat_completion_multiple_tools_streaming(
+        span_exporter, log_exporter, async_openai_client, True
+    )
+
+
+@pytest.mark.vcr()
+@pytest.mark.asyncio()
+async def test_async_chat_completion_multiple_tools_streaming_no_content(
+    span_exporter, log_exporter, async_openai_client, instrument_no_content
+):
+    await async_chat_completion_multiple_tools_streaming(
+        span_exporter, log_exporter, async_openai_client, False
+    )
+
+
+async def async_chat_completion_multiple_tools_streaming(
+    span_exporter, log_exporter, async_openai_client, expect_content
+):
+    llm_model_value = "gpt-4o-mini"
+    messages_value = [
+        {"role": "system", "content": "You're a helpful assistant."},
+        {
+            "role": "user",
+            "content": "What's the weather in Seattle and San Francisco today?",
+        },
+    ]
+
+    response = await async_openai_client.chat.completions.create(
+        messages=messages_value,
+        model=llm_model_value,
+        tool_choice="auto",
+        tools=[get_current_weather_tool_definition()],
+        stream=True,
+        stream_options={"include_usage": True},
+    )
+
+    finish_reason = None
+    # two tools
+    tool_names = ["", ""]
+    tool_call_ids = ["", ""]
+    tool_args = ["", ""]
+    async for chunk in response:
+        if chunk.choices:
+            if chunk.choices[0].finish_reason:
+                finish_reason = chunk.choices[0].finish_reason
+            for tool_call in chunk.choices[0].delta.tool_calls or []:
+                t_idx = tool_call.index
+                if tool_call.id:
+                    tool_call_ids[t_idx] = tool_call.id
+                if tool_call.function:
+                    if tool_call.function.arguments:
+                        tool_args[t_idx] += tool_call.function.arguments
+                    if tool_call.function.name:
+                        tool_names[t_idx] = tool_call.function.name
+
+        # get the last chunk
+        if getattr(chunk, "usage", None):
+            response_stream_usage = chunk.usage
+            response_stream_model = chunk.model
+            response_stream_id = chunk.id
+
+    # sanity check
+    assert "tool_calls" == finish_reason
+
+    spans = span_exporter.get_finished_spans()
+    assert_all_attributes(
+        spans[0],
+        llm_model_value,
+        response_stream_id,
+        response_stream_model,
+        response_stream_usage.prompt_tokens,
+        response_stream_usage.completion_tokens,
+    )
+
+    logs = log_exporter.get_finished_logs()
+    assert len(logs) == 3
+
+    system_message = (
+        {"content": messages_value[0]["content"]} if expect_content else None
+    )
+    assert_message_in_logs(
+        logs[0], "gen_ai.system.message", system_message, spans[0]
+    )
+
+    user_message = (
+        {"content": "What's the weather in Seattle and San Francisco today?"}
+        if expect_content
+        else None
+    )
+    assert_message_in_logs(
+        logs[1], "gen_ai.user.message", user_message, spans[0]
+    )
+
+    choice_event = {
+        "index": 0,
+        "finish_reason": "tool_calls",
+        "message": {
+            "role": "assistant",
+            "tool_calls": [
+                {
+                    "id": tool_call_ids[0],
+                    "type": "function",
+                    "function": {
+                        "name": tool_names[0],
+                        "arguments": (
+                            tool_args[0].replace("\n", "")
+                            if expect_content
+                            else None
+                        ),
+                    },
+                },
+                {
+                    "id": tool_call_ids[1],
+                    "type": "function",
+                    "function": {
+                        "name": tool_names[1],
+                        "arguments": (
+                            tool_args[1].replace("\n", "")
+                            if expect_content
+                            else None
+                        ),
+                    },
+                },
+            ],
+        },
+    }
+    assert_message_in_logs(logs[2], "gen_ai.choice", choice_event, spans[0])
+
+
+def assert_message_in_logs(log, event_name, expected_content, parent_span):
+    assert log.log_record.attributes[EventAttributes.EVENT_NAME] == event_name
+    assert (
+        log.log_record.attributes[GenAIAttributes.GEN_AI_SYSTEM]
+        == GenAIAttributes.GenAiSystemValues.OPENAI.value
+    )
+
+    if not expected_content:
+        assert not log.log_record.body
+    else:
+        assert log.log_record.body
+        assert dict(log.log_record.body) == remove_none_values(
+            expected_content
+        )
+    assert_log_parent(log, parent_span)
+
+
+def remove_none_values(body):
+    result = {}
+    for key, value in body.items():
+        if value is None:
+            continue
+        if isinstance(value, dict):
+            result[key] = remove_none_values(value)
+        elif isinstance(value, list):
+            result[key] = [remove_none_values(i) for i in value]
+        else:
+            result[key] = value
+    return result
+
+
+def assert_completion_attributes(
+    span: ReadableSpan,
+    request_model: str,
+    response: ChatCompletion,
+    operation_name: str = "chat",
+    server_address: str = "api.openai.com",
+):
+    return assert_all_attributes(
+        span,
+        request_model,
+        response.id,
+        response.model,
+        response.usage.prompt_tokens,
+        response.usage.completion_tokens,
+        operation_name,
+        server_address,
+    )
+
+
+def assert_all_attributes(
+    span: ReadableSpan,
+    request_model: str,
+    response_id: str = None,
+    response_model: str = None,
+    input_tokens: Optional[int] = None,
+    output_tokens: Optional[int] = None,
+    operation_name: str = "chat",
+    server_address: str = "api.openai.com",
+):
+    assert span.name == f"{operation_name} {request_model}"
+    assert (
+        operation_name
+        == span.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME]
+    )
+    assert (
+        GenAIAttributes.GenAiSystemValues.OPENAI.value
+        == span.attributes[GenAIAttributes.GEN_AI_SYSTEM]
+    )
+    assert (
+        request_model == span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL]
+    )
+    if response_model:
+        assert (
+            response_model
+            == span.attributes[GenAIAttributes.GEN_AI_RESPONSE_MODEL]
+        )
+    else:
+        assert GenAIAttributes.GEN_AI_RESPONSE_MODEL not in span.attributes
+
+    if response_id:
+        assert (
+            response_id == span.attributes[GenAIAttributes.GEN_AI_RESPONSE_ID]
+        )
+    else:
+        assert GenAIAttributes.GEN_AI_RESPONSE_ID not in span.attributes
+
+    if input_tokens:
+        assert (
+            input_tokens
+            == span.attributes[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS]
+        )
+    else:
+        assert GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS not in span.attributes
+
+    if output_tokens:
+        assert (
+            output_tokens
+            == span.attributes[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS]
+        )
+    else:
+        assert (
+            GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS not in span.attributes
+        )
+
+    assert server_address == span.attributes[ServerAttributes.SERVER_ADDRESS]
+
+
+def assert_log_parent(log, span):
+    assert log.log_record.trace_id == span.get_span_context().trace_id
+    assert log.log_record.span_id == span.get_span_context().span_id
+    assert log.log_record.trace_flags == span.get_span_context().trace_flags
+
+
+def get_current_weather_tool_definition():
+    return {
+        "type": "function",
+        "function": {
+            "name": "get_current_weather",
+            "description": "Get the current weather in a given location",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "The city and state, e.g. Boston, MA",
+                    },
+                },
+                "required": ["location"],
+                "additionalProperties": False,
+            },
+        },
+    }