inference instrumentor to inference library

.vscode/cspell.json

@@ -406,6 +406,9 @@
     "uamqp",
     "uksouth",
     "ukwest",
+    "uninstrument",
+    "uninstrumented",
+    "uninstrumenting",
     "unpad",
     "unpadder",
     "unpartial",

sdk/ai/azure-ai-inference/CHANGELOG.md

@@ -4,6 +4,8 @@
 
 ### Features Added
 
+* Support for tracing. Please find more information in the package [README.md](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/ai/azure-ai-inference/README.md).
+
 ### Breaking Changes
 
 ### Bugs Fixed

sdk/ai/azure-ai-inference/README.md

@@ -57,6 +57,14 @@ To update an existing installation of the package, use:
 pip install --upgrade azure-ai-inference
 ```
 
+If you want to install Azure AI Inferencing package with support for OpenTelemetry based tracing, use the following command:
+
+```bash
+pip install azure-ai-inference[trace]
+```
+
+
+
 ## Key concepts
 
 ### Create and authenticate a client directly, using API key or GitHub token
@@ -530,6 +538,87 @@ For more information, see [Configure logging in the Azure libraries for Python](
 
 To report issues with the client library, or request additional features, please open a GitHub issue [here](https://github.com/Azure/azure-sdk-for-python/issues)
 
+## Tracing
+
+The Azure AI Inferencing API Tracing library provides tracing for Azure AI Inference client library for Python. Refer to Installation chapter above for installation instructions.
+
+### Setup
+
+The environment variable AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED controls whether the actual message contents will be recorded in the traces or not. By default, the message contents are not recorded as part of the trace. When message content recording is disabled any function call tool related function names, function parameter names and function parameter values are also not recorded in the trace. Set the value of the environment variable to "true" (case insensitive) for the message contents to be recorded as part of the trace. Any other value will cause the message contents not to be recorded.
+
+You also need to configure the tracing implementation in your code by setting `AZURE_SDK_TRACING_IMPLEMENTATION` to `opentelemetry` or configuring it in the code with the following snippet:
+
+<!-- SNIPPET:sample_chat_completions_with_tracing.trace_setting -->
+
+```python
+from azure.core.settings import settings
+settings.tracing_implementation = "opentelemetry"
+```
+
+<!-- END SNIPPET -->
+
+Please refer to [azure-core-tracing-documentation](https://learn.microsoft.com/python/api/overview/azure/core-tracing-opentelemetry-readme) for more information.
+
+### Exporting Traces with OpenTelemetry
+
+Azure AI Inference is instrumented with OpenTelemetry. In order to enable tracing you need to configure OpenTelemetry to export traces to your observability backend. 
+Refer to [Azure SDK tracing in Python](https://learn.microsoft.com/python/api/overview/azure/core-tracing-opentelemetry-readme?view=azure-python-preview) for more details.
+
+Refer to [Azure Monitor OpenTelemetry documentation](https://learn.microsoft.com/azure/azure-monitor/app/opentelemetry-enable?tabs=python) for the details on how to send Azure AI Inference traces to Azure Monitor and create Azure Monitor resource.
+
+### Instrumentation
+
+Use the AIInferenceInstrumentor to instrument the Azure AI Inferencing API for LLM tracing, this will cause the LLM traces to be emitted from Azure AI Inferencing API.
+
+<!-- SNIPPET:sample_chat_completions_with_tracing.instrument_inferencing -->
+
+```python
+from azure.ai.inference.tracing import AIInferenceInstrumentor
+# Instrument AI Inference API
+AIInferenceInstrumentor().instrument()
+```
+
+<!-- END SNIPPET -->
+
+
+It is also possible to uninstrument the Azure AI Inferencing API by using the uninstrument call. After this call, the traces will no longer be emitted by the Azure AI Inferencing API until instrument is called again.
+
+<!-- SNIPPET:sample_chat_completions_with_tracing.uninstrument_inferencing -->
+
+```python
+AIInferenceInstrumentor().uninstrument()
+```
+
+<!-- END SNIPPET -->
+
+### Tracing Your Own Functions
+The @tracer.start_as_current_span decorator can be used to trace your own functions. This will trace the function parameters and their values. You can also add further attributes to the span in the function implementation as demonstrated below. Note that you will have to setup the tracer in your code before using the decorator. More information is available [here](https://opentelemetry.io/docs/languages/python/).
+
+<!-- SNIPPET:sample_chat_completions_with_tracing.trace_function -->
+
+```python
+from opentelemetry.trace import get_tracer
+tracer = get_tracer(__name__)
+
+# The tracer.start_as_current_span decorator will trace the function call and enable adding additional attributes
+# to the span in the function implementation. Note that this will trace the function parameters and their values.
+@tracer.start_as_current_span("get_temperature") # type: ignore
+def get_temperature(city: str) -> str:
+
+    # Adding attributes to the current span
+    span = trace.get_current_span()
+    span.set_attribute("requested_city", city)
+
+    if city == "Seattle":
+        return "75"
+    elif city == "New York City":
+        return "80"
+    else:
+        return "Unavailable"
+```
+
+<!-- END SNIPPET -->
+
 ## Next steps
 
 * Have a look at the [Samples](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/ai/azure-ai-inference/samples) folder, containing fully runnable Python code for doing inference using synchronous and asynchronous clients.

sdk/ai/azure-ai-inference/assets.json

@@ -2,5 +2,5 @@
   "AssetsRepo": "Azure/azure-sdk-assets",
   "AssetsRepoPrefixPath": "python",
   "TagPrefix": "python/ai/azure-ai-inference",
-  "Tag": "python/ai/azure-ai-inference_498e85cbfd"
+  "Tag": "python/ai/azure-ai-inference_19a0adafc6"
 }

sdk/ai/azure-ai-inference/azure/ai/inference/_patch.py

@@ -102,8 +102,8 @@ def load_client(
             "The AI model information is missing a value for `model type`. Cannot create an appropriate client."
         )
 
-    # TODO: Remove "completions" and "embedding" once Mistral Large and Cohere fixes their model type
-    if model_info.model_type in (_models.ModelType.CHAT, "completion"):
+    # TODO: Remove "completions", "chat-comletions" and "embedding" once Mistral Large and Cohere fixes their model type
+    if model_info.model_type in (_models.ModelType.CHAT, "completion", "chat-completion", "chat-completions"):
         chat_completion_client = ChatCompletionsClient(endpoint, credential, **kwargs)
         chat_completion_client._model_info = (  # pylint: disable=protected-access,attribute-defined-outside-init
             model_info
@@ -454,7 +454,7 @@ def complete(
         :raises ~azure.core.exceptions.HttpResponseError:
         """
 
-    @distributed_trace
+    # pylint:disable=client-method-missing-tracing-decorator
     def complete(
         self,
         body: Union[JSON, IO[bytes]] = _Unset,

sdk/ai/azure-ai-inference/azure/ai/inference/aio/_patch.py

@@ -87,7 +87,7 @@ async def load_client(
         )
 
     # TODO: Remove "completions" and "embedding" once Mistral Large and Cohere fixes their model type
-    if model_info.model_type in (_models.ModelType.CHAT, "completion"):
+    if model_info.model_type in (_models.ModelType.CHAT, "completion", "chat-completion", "chat-completions"):
         chat_completion_client = ChatCompletionsClient(endpoint, credential, **kwargs)
         chat_completion_client._model_info = (  # pylint: disable=protected-access,attribute-defined-outside-init
             model_info
@@ -437,7 +437,7 @@ async def complete(
         :raises ~azure.core.exceptions.HttpResponseError:
         """
 
-    @distributed_trace_async
+    # pylint:disable=client-method-missing-tracing-decorator-async
     async def complete(
         self,
         body: Union[JSON, IO[bytes]] = _Unset,

sdk/ai/azure-ai-inference/azure/ai/inference/models/_patch.py

@@ -14,7 +14,7 @@
 import re
 import sys
 
-from typing import List, AsyncIterator, Iterator, Optional, Union
+from typing import Any, List, AsyncIterator, Iterator, Optional, Union
 from azure.core.rest import HttpResponse, AsyncHttpResponse
 from ._models import ImageUrl as ImageUrlGenerated
 from ._models import ChatCompletions as ChatCompletionsGenerated
@@ -200,7 +200,7 @@ def __init__(self, response: HttpResponse):
         self._response = response
         self._bytes_iterator: Iterator[bytes] = response.iter_bytes()
 
-    def __iter__(self):
+    def __iter__(self) -> Any:
         return self
 
     def __next__(self) -> "_models.StreamingChatCompletionsUpdate":
@@ -220,7 +220,7 @@ def _read_next_block(self) -> bool:
             return True
         return self._deserialize_and_add_to_queue(element)
 
-    def __exit__(self, exc_type, exc_val, exc_tb) -> None:
+    def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None:  # type: ignore
         self.close()
 
     def close(self) -> None:
@@ -239,7 +239,7 @@ def __init__(self, response: AsyncHttpResponse):
         self._response = response
         self._bytes_iterator: AsyncIterator[bytes] = response.iter_bytes()
 
-    def __aiter__(self):
+    def __aiter__(self) -> Any:
         return self
 
     async def __anext__(self) -> "_models.StreamingChatCompletionsUpdate":
@@ -259,7 +259,7 @@ async def _read_next_block_async(self) -> bool:
             return True
         return self._deserialize_and_add_to_queue(element)
 
-    def __exit__(self, exc_type, exc_val, exc_tb) -> None:
+    def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None:  # type: ignore
         asyncio.run(self.aclose())
 
     async def aclose(self) -> None: