Python: Update gen_ai traces and logs (#10173)

### Motivation and Context

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The gen_ai semantic convention has gone through some updates since we
started generating telemetry for gen_ai operations. We would like to
align with the latest conventions, and most importantly allow our users
to visualize the gen_ai traces on the Azure AI Foundry Tracing UI, which
relies on the gen_ai conventions.

### Description

<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->

1. Updates the gen_ai telemetry module to align with the latest gen_ai
convention so that all AI connectors generate telemetry data that can be
visualized on Azure AI Foundry.
2. Unit tests for the update.

> Note that this is a breaking change as this feature is still
experimental. Anyone who is relying on the previous gen_ai conventions
should also update.

### Contribution Checklist

<!-- Before submitting this PR, please make sure: -->

- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄

python/semantic_kernel/utils/telemetry/model_diagnostics/decorators.py

@@ -2,9 +2,10 @@
 
 import functools
 import json
+import logging
 from collections.abc import AsyncGenerator, Callable
 from functools import reduce
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, ClassVar
 
 from opentelemetry.trace import Span, StatusCode, get_tracer, use_span
 
@@ -37,9 +38,36 @@
 TEXT_COMPLETION_OPERATION = "text.completions"
 TEXT_STREAMING_COMPLETION_OPERATION = "text.streaming_completions"
 
+
+# We're recording multiple events for the chat history, some of them are emitted within (hundreds of)
+# nanoseconds of each other. The default timestamp resolution is not high enough to guarantee unique
+# timestamps for each message. Also Azure Monitor truncates resolution to microseconds and some other
+# backends truncate to milliseconds.
+#
+# But we need to give users a way to restore chat message order, so we're incrementing the timestamp
+# by 1 microsecond for each message.
+#
+# This is a workaround, we'll find a generic and better solution - see
+# https://github.com/open-telemetry/semantic-conventions/issues/1701
+class ChatHistoryMessageTimestampFilter(logging.Filter):
+    """A filter to increment the timestamp of INFO logs by 1 microsecond."""
+
+    INDEX_KEY: ClassVar[str] = "CHAT_MESSAGE_INDEX"
+
+    def filter(self, record: logging.LogRecord) -> bool:
+        """Increment the timestamp of INFO logs by 1 microsecond."""
+        if hasattr(record, self.INDEX_KEY):
+            idx = getattr(record, self.INDEX_KEY)
+            record.created += idx * 1e-6
+        return True
+
+
 # Creates a tracer from the global tracer provider
 tracer = get_tracer(__name__)
 
+logger = logging.getLogger(__name__)
+logger.addFilter(ChatHistoryMessageTimestampFilter())
+
 
 @experimental_function
 def are_model_diagnostics_enabled() -> bool:
@@ -87,19 +115,19 @@ async def wrapper_decorator(*args: Any, **kwargs: Any) -> list[ChatMessageConten
             settings: "PromptExecutionSettings" = kwargs.get("settings") or args[2]  # type: ignore
 
             with use_span(
-                _start_completion_activity(
+                _get_completion_span(
                     CHAT_COMPLETION_OPERATION,
                     completion_service.ai_model_id,
                     model_provider,
                     completion_service.service_url(),
-                    chat_history,
                     settings,
                 ),
                 end_on_exit=True,
             ) as current_span:
+                _set_completion_input(model_provider, chat_history)
                 try:
                     completions: list[ChatMessageContent] = await completion_func(*args, **kwargs)
-                    _set_completion_response(current_span, completions)
+                    _set_completion_response(current_span, completions, model_provider)
                     return completions
                 except Exception as exception:
                     _set_completion_error(current_span, exception)
@@ -144,16 +172,16 @@ async def wrapper_decorator(
             all_messages: dict[int, list[StreamingChatMessageContent]] = {}
 
             with use_span(
-                _start_completion_activity(
+                _get_completion_span(
                     CHAT_STREAMING_COMPLETION_OPERATION,
                     completion_service.ai_model_id,
                     model_provider,
                     completion_service.service_url(),
-                    chat_history,
                     settings,
                 ),
                 end_on_exit=True,
             ) as current_span:
+                _set_completion_input(model_provider, chat_history)
                 try:
                     async for streaming_chat_message_contents in completion_func(*args, **kwargs):
                         for streaming_chat_message_content in streaming_chat_message_contents:
@@ -166,7 +194,7 @@ async def wrapper_decorator(
                     all_messages_flattened = [
                         reduce(lambda x, y: x + y, messages) for messages in all_messages.values()
                     ]
-                    _set_completion_response(current_span, all_messages_flattened)
+                    _set_completion_response(current_span, all_messages_flattened, model_provider)
                 except Exception as exception:
                     _set_completion_error(current_span, exception)
                     raise
@@ -203,19 +231,19 @@ async def wrapper_decorator(*args: Any, **kwargs: Any) -> list[TextContent]:
             settings: "PromptExecutionSettings" = kwargs["settings"] if kwargs.get("settings") is not None else args[2]
 
             with use_span(
-                _start_completion_activity(
+                _get_completion_span(
                     TEXT_COMPLETION_OPERATION,
                     completion_service.ai_model_id,
                     model_provider,
                     completion_service.service_url(),
-                    prompt,
                     settings,
                 ),
                 end_on_exit=True,
             ) as current_span:
+                _set_completion_input(model_provider, prompt)
                 try:
                     completions: list[TextContent] = await completion_func(*args, **kwargs)
-                    _set_completion_response(current_span, completions)
+                    _set_completion_response(current_span, completions, model_provider)
                     return completions
                 except Exception as exception:
                     _set_completion_error(current_span, exception)
@@ -258,16 +286,16 @@ async def wrapper_decorator(*args: Any, **kwargs: Any) -> AsyncGenerator[list["S
             all_text_contents: dict[int, list["StreamingTextContent"]] = {}
 
             with use_span(
-                _start_completion_activity(
+                _get_completion_span(
                     TEXT_STREAMING_COMPLETION_OPERATION,
                     completion_service.ai_model_id,
                     model_provider,
                     completion_service.service_url(),
-                    prompt,
                     settings,
                 ),
                 end_on_exit=True,
             ) as current_span:
+                _set_completion_input(model_provider, prompt)
                 try:
                     async for streaming_text_contents in completion_func(*args, **kwargs):
                         for streaming_text_content in streaming_text_contents:
@@ -280,7 +308,7 @@ async def wrapper_decorator(*args: Any, **kwargs: Any) -> AsyncGenerator[list["S
                     all_text_contents_flattened = [
                         reduce(lambda x, y: x + y, messages) for messages in all_text_contents.values()
                     ]
-                    _set_completion_response(current_span, all_text_contents_flattened)
+                    _set_completion_response(current_span, all_text_contents_flattened, model_provider)
                 except Exception as exception:
                     _set_completion_error(current_span, exception)
                     raise
@@ -292,15 +320,18 @@ async def wrapper_decorator(*args: Any, **kwargs: Any) -> AsyncGenerator[list["S
     return inner_trace_streaming_text_completion
 
 
-def _start_completion_activity(
+def _get_completion_span(
     operation_name: str,
     model_name: str,
     model_provider: str,
     service_url: str | None,
-    prompt: str | ChatHistory,
     execution_settings: "PromptExecutionSettings | None",
 ) -> Span:
-    """Start a text or chat completion activity for a given model."""
+    """Start a text or chat completion span for a given model.
+
+    Note that `start_span` doesn't make the span the current span.
+    Use `use_span` to make it the current span as a context manager.
+    """
     span = tracer.start_span(f"{operation_name} {model_name}")
 
     # Set attributes on the span
@@ -316,24 +347,53 @@ def _start_completion_activity(
     # TODO(@glahaye): we'll need to have a way to get these attributes from model
     # providers other than OpenAI (for example if the attributes are named differently)
     if execution_settings:
-        attribute = execution_settings.extension_data.get("max_tokens")
-        if attribute:
-            span.set_attribute(gen_ai_attributes.MAX_TOKENS, attribute)
+        attribute_name_map = {
+            "seed": gen_ai_attributes.SEED,
+            "encoding_formats": gen_ai_attributes.ENCODING_FORMATS,
+            "frequency_penalty": gen_ai_attributes.FREQUENCY_PENALTY,
+            "max_tokens": gen_ai_attributes.MAX_TOKENS,
+            "stop_sequences": gen_ai_attributes.STOP_SEQUENCES,
+            "temperature": gen_ai_attributes.TEMPERATURE,
+            "top_k": gen_ai_attributes.TOP_K,
+            "top_p": gen_ai_attributes.TOP_P,
+        }
+        for attribute_name, attribute_key in attribute_name_map.items():
+            attribute = execution_settings.extension_data.get(attribute_name)
+            if attribute:
+                span.set_attribute(attribute_key, attribute)
+
+    return span
 
-        attribute = execution_settings.extension_data.get("temperature")
-        if attribute:
-            span.set_attribute(gen_ai_attributes.TEMPERATURE, attribute)
 
-        attribute = execution_settings.extension_data.get("top_p")
-        if attribute:
-            span.set_attribute(gen_ai_attributes.TOP_P, attribute)
+def _set_completion_input(
+    model_provider: str,
+    prompt: str | ChatHistory,
+) -> None:
+    """Set the input for a text or chat completion.
 
+    The logs will be associated to the current span.
+    """
     if are_sensitive_events_enabled():
         if isinstance(prompt, ChatHistory):
-            prompt = _messages_to_openai_format(prompt.messages)
-        span.add_event(gen_ai_attributes.PROMPT_EVENT, {gen_ai_attributes.PROMPT_EVENT_PROMPT: prompt})
-
-    return span
+            for idx, message in enumerate(prompt.messages):
+                event_name = gen_ai_attributes.ROLE_EVENT_MAP.get(message.role)
+                if event_name:
+                    logger.info(
+                        json.dumps(message.to_dict()),
+                        extra={
+                            gen_ai_attributes.EVENT_NAME: event_name,
+                            gen_ai_attributes.SYSTEM: model_provider,
+                            ChatHistoryMessageTimestampFilter.INDEX_KEY: idx,
+                        },
+                    )
+        else:
+            logger.info(
+                prompt,
+                extra={
+                    gen_ai_attributes.EVENT_NAME: gen_ai_attributes.PROMPT,
+                    gen_ai_attributes.SYSTEM: model_provider,
+                },
+            )
 
 
 def _set_completion_response(
@@ -342,8 +402,9 @@ def _set_completion_response(
     | list[TextContent]
     | list[StreamingChatMessageContent]
     | list[StreamingTextContent],
+    model_provider: str,
 ) -> None:
-    """Set the a text or chat completion response for a given activity."""
+    """Set the a text or chat completion response for a given span."""
     first_completion = completions[0]
 
     # Set the response ID
@@ -362,33 +423,32 @@ def _set_completion_response(
     usage = first_completion.metadata.get("usage", None)
     if isinstance(usage, CompletionUsage):
         if usage.prompt_tokens:
-            current_span.set_attribute(gen_ai_attributes.PROMPT_TOKENS, usage.prompt_tokens)
+            current_span.set_attribute(gen_ai_attributes.INPUT_TOKENS, usage.prompt_tokens)
         if usage.completion_tokens:
-            current_span.set_attribute(gen_ai_attributes.COMPLETION_TOKENS, usage.completion_tokens)
+            current_span.set_attribute(gen_ai_attributes.OUTPUT_TOKENS, usage.completion_tokens)
 
     # Set the completion event
     if are_sensitive_events_enabled():
-        completion_text: str = _messages_to_openai_format(completions)
-        current_span.add_event(
-            gen_ai_attributes.COMPLETION_EVENT, {gen_ai_attributes.COMPLETION_EVENT_COMPLETION: completion_text}
-        )
+        for completion in completions:
+            full_response: dict[str, Any] = {
+                "message": completion.to_dict(),
+            }
+
+            if hasattr(completion, "finish_reason"):
+                full_response["finish_reason"] = completion.finish_reason
+            if hasattr(completion, "choice_index"):
+                full_response["index"] = completion.choice_index
+
+            logger.info(
+                json.dumps(full_response),
+                extra={
+                    gen_ai_attributes.EVENT_NAME: gen_ai_attributes.CHOICE,
+                    gen_ai_attributes.SYSTEM: model_provider,
+                },
+            )
 
 
 def _set_completion_error(span: Span, error: Exception) -> None:
     """Set an error for a text or chat completion ."""
     span.set_attribute(gen_ai_attributes.ERROR_TYPE, str(type(error)))
     span.set_status(StatusCode.ERROR, repr(error))
-
-
-def _messages_to_openai_format(
-    messages: list[ChatMessageContent]
-    | list[StreamingChatMessageContent]
-    | list[TextContent]
-    | list[StreamingTextContent],
-) -> str:
-    """Convert a list of ChatMessageContent to a string in the OpenAI format.
-
-    OpenTelemetry recommends formatting the messages in the OpenAI format
-    regardless of the actual model being used.
-    """
-    return json.dumps([message.to_dict() for message in messages])

python/semantic_kernel/utils/telemetry/model_diagnostics/gen_ai_attributes.py

@@ -1,32 +1,48 @@
 # Copyright (c) Microsoft. All rights reserved.
 
+from semantic_kernel.contents.utils.author_role import AuthorRole
+
 # Constants for tracing activities with semantic conventions.
 # Ideally, we should use the attributes from the semcov package.
 # However, many of the attributes are not yet available in the package,
 # so we define them here for now.
 
 # Activity tags
-SYSTEM = "gen_ai.system"
 OPERATION = "gen_ai.operation.name"
+SYSTEM = "gen_ai.system"
+ERROR_TYPE = "error.type"
 MODEL = "gen_ai.request.model"
-MAX_TOKENS = "gen_ai.request.max_tokens"  # nosec
+SEED = "gen_ai.request.seed"
+PORT = "server.port"
+ENCODING_FORMATS = "gen_ai.request.encoding_formats"
+FREQUENCY_PENALTY = "gen_ai.request.frequency_penalty"
+MAX_TOKENS = "gen_ai.request.max_tokens"
+STOP_SEQUENCES = "gen_ai.request.stop_sequences"
 TEMPERATURE = "gen_ai.request.temperature"
+TOP_K = "gen_ai.request.top_k"
 TOP_P = "gen_ai.request.top_p"
-RESPONSE_ID = "gen_ai.response.id"
 FINISH_REASON = "gen_ai.response.finish_reason"
-PROMPT_TOKENS = "gen_ai.response.prompt_tokens"  # nosec
-COMPLETION_TOKENS = "gen_ai.response.completion_tokens"  # nosec
+RESPONSE_ID = "gen_ai.response.id"
+INPUT_TOKENS = "gen_ai.usage.input_tokens"
+OUTPUT_TOKENS = "gen_ai.usage.output_tokens"
 ADDRESS = "server.address"
-PORT = "server.port"
-ERROR_TYPE = "error.type"
 
 # Activity events
-PROMPT_EVENT = "gen_ai.content.prompt"
-COMPLETION_EVENT = "gen_ai.content.completion"
-
-# Activity event attributes
-PROMPT_EVENT_PROMPT = "gen_ai.prompt"
-COMPLETION_EVENT_COMPLETION = "gen_ai.completion"
+EVENT_NAME = "event.name"
+SYSTEM_MESSAGE = "gen_ai.system.message"
+USER_MESSAGE = "gen_ai.user.message"
+ASSISTANT_MESSAGE = "gen_ai.assistant.message"
+TOOL_MESSAGE = "gen_ai.tool.message"
+CHOICE = "gen_ai.choice"
+PROMPT = "gen_ai.prompt"
 
 # Kernel specific attributes
 AVAILABLE_FUNCTIONS = "sk.available_functions"
+
+
+ROLE_EVENT_MAP = {
+    AuthorRole.SYSTEM: SYSTEM_MESSAGE,
+    AuthorRole.USER: USER_MESSAGE,
+    AuthorRole.ASSISTANT: ASSISTANT_MESSAGE,
+    AuthorRole.TOOL: TOOL_MESSAGE,
+}