OpenAI - update semantic conventions to the latest version, write logs based events instead of span events (#2925)
This commit is contained in:
Liudmila Molkova
GitHub
parent
54c7ee80ab
commit
07c3324a3b
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@ -0,0 +1,15 @@
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# Changelog
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All notable changes to this project will be documented in this file.
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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## Unreleased
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- Update OpenAI instrumentation to Semantic Conventions v1.28.0: add new attributes
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and switch prompts and completions to log-based events.
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([#2925](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/2925))
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- Initial OpenAI instrumentation
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([#2759](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/2759))
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@ -1,12 +1,12 @@
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OpenTelemetry OpenAI Instrumentation
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====================================
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====================================
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|pypi|
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.. |pypi| image:: https://badge.fury.io/py/opentelemetry-instrumentation-openai-v2.svg
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:target: https://pypi.org/project/opentelemetry-instrumentation-openai-v2/
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Instrumentation with OpenAI that supports the openai library and is
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Instrumentation with OpenAI that supports the OpenAI library and is
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specified to trace_integration using 'OpenAI'.
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@ -24,7 +24,7 @@ Usage
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.. code:: python
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from openai import OpenAI
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from opentelemetry.instrumentation.openai import OpenAIInstrumentor
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from opentelemetry.instrumentation.openai_v2 import OpenAIInstrumentor
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OpenAIInstrumentor().instrument()
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@ -44,8 +44,10 @@ from typing import Collection
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from wrapt import wrap_function_wrapper
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from opentelemetry._events import get_event_logger
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from opentelemetry.instrumentation.instrumentor import BaseInstrumentor
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from opentelemetry.instrumentation.openai_v2.package import _instruments
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from opentelemetry.instrumentation.openai_v2.utils import is_content_enabled
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from opentelemetry.instrumentation.utils import unwrap
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from opentelemetry.semconv.schemas import Schemas
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from opentelemetry.trace import get_tracer
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@ -64,15 +66,25 @@ class OpenAIInstrumentor(BaseInstrumentor):
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__name__,
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"",
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tracer_provider,
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schema_url=Schemas.V1_27_0.value,
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schema_url=Schemas.V1_28_0.value,
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)
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event_logger_provider = kwargs.get("event_logger_provider")
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event_logger = get_event_logger(
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__name__,
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"",
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schema_url=Schemas.V1_28_0.value,
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event_logger_provider=event_logger_provider,
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)
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wrap_function_wrapper(
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module="openai.resources.chat.completions",
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name="Completions.create",
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wrapper=chat_completions_create(tracer),
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wrapper=chat_completions_create(
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tracer, event_logger, is_content_enabled()
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),
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)
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def _uninstrument(self, **kwargs):
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import openai
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import openai # pylint: disable=import-outside-toplevel
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unwrap(openai.resources.chat.completions.Completions, "create")
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@ -13,8 +13,11 @@
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# limitations under the License.
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import json
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from typing import Optional
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from openai import Stream
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from opentelemetry._events import Event, EventLogger
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from opentelemetry.semconv._incubating.attributes import (
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gen_ai_attributes as GenAIAttributes,
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)
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@ -25,97 +28,73 @@ from opentelemetry.trace import Span, SpanKind, Tracer
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from opentelemetry.trace.status import Status, StatusCode
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from .utils import (
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extract_content,
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extract_tools_prompt,
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choice_to_event,
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get_llm_request_attributes,
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is_streaming,
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set_event_completion,
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set_event_prompt,
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message_to_event,
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set_span_attribute,
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silently_fail,
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)
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def chat_completions_create(tracer: Tracer):
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def chat_completions_create(
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tracer: Tracer, event_logger: EventLogger, capture_content: bool
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):
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"""Wrap the `create` method of the `ChatCompletion` class to trace it."""
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def traced_method(wrapped, instance, args, kwargs):
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llm_prompts = []
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span_attributes = {**get_llm_request_attributes(kwargs, instance)}
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for item in kwargs.get("messages", []):
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tools_prompt = extract_tools_prompt(item)
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llm_prompts.append(tools_prompt if tools_prompt else item)
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span_attributes = {**get_llm_request_attributes(kwargs)}
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span_name = f"{span_attributes[GenAIAttributes.GEN_AI_OPERATION_NAME]} {span_attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL]}"
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with tracer.start_as_current_span(
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name=span_name,
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kind=SpanKind.CLIENT,
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attributes=span_attributes,
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end_on_exit=False,
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) as span:
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if span.is_recording():
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for message in kwargs.get("messages", []):
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event_logger.emit(
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message_to_event(message, capture_content)
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)
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span = tracer.start_span(
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name=span_name, kind=SpanKind.CLIENT, attributes=span_attributes
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)
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if span.is_recording():
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_set_input_attributes(span, span_attributes)
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set_event_prompt(span, json.dumps(llm_prompts))
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try:
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result = wrapped(*args, **kwargs)
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if is_streaming(kwargs):
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return StreamWrapper(
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result, span, event_logger, capture_content
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)
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try:
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result = wrapped(*args, **kwargs)
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if is_streaming(kwargs):
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return StreamWrapper(
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result,
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span,
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function_call=kwargs.get("functions") is not None,
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tool_calls=kwargs.get("tools") is not None,
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)
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else:
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if span.is_recording():
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_set_response_attributes(span, result)
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_set_response_attributes(
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span, result, event_logger, capture_content
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)
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span.end()
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return result
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except Exception as error:
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span.set_status(Status(StatusCode.ERROR, str(error)))
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if span.is_recording():
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span.set_attribute(
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ErrorAttributes.ERROR_TYPE, type(error).__qualname__
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)
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span.end()
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raise
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except Exception as error:
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span.set_status(Status(StatusCode.ERROR, str(error)))
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if span.is_recording():
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span.set_attribute(
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ErrorAttributes.ERROR_TYPE, type(error).__qualname__
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)
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span.end()
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raise
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return traced_method
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@silently_fail
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def _set_input_attributes(span, attributes):
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for field, value in attributes.items():
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set_span_attribute(span, field, value)
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@silently_fail
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def _set_response_attributes(span, result):
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def _set_response_attributes(
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span, result, event_logger: EventLogger, capture_content: bool
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):
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set_span_attribute(
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span, GenAIAttributes.GEN_AI_RESPONSE_MODEL, result.model
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)
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if getattr(result, "choices", None):
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choices = result.choices
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responses = [
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{
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"role": (
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choice.message.role
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if choice.message and choice.message.role
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else "assistant"
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),
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"content": extract_content(choice),
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**(
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{
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"content_filter_results": choice[
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"content_filter_results"
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]
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}
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if "content_filter_results" in choice
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else {}
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),
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}
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for choice in choices
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]
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for choice in choices:
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event_logger.emit(choice_to_event(choice, capture_content))
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finish_reasons = []
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for choice in choices:
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finish_reasons.append(choice.finish_reason or "error")
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@ -125,11 +104,17 @@ def _set_response_attributes(span, result):
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GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS,
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finish_reasons,
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)
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set_event_completion(span, responses)
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if getattr(result, "id", None):
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set_span_attribute(span, GenAIAttributes.GEN_AI_RESPONSE_ID, result.id)
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if getattr(result, "service_tier", None):
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set_span_attribute(
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span,
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GenAIAttributes.GEN_AI_OPENAI_REQUEST_SERVICE_TIER,
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result.service_tier,
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)
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# Get the usage
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if getattr(result, "usage", None):
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set_span_attribute(
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@ -144,27 +129,65 @@ def _set_response_attributes(span, result):
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)
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class ToolCallBuffer:
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def __init__(self, index, tool_call_id, function_name):
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self.index = index
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self.function_name = function_name
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self.tool_call_id = tool_call_id
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self.arguments = []
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def append_arguments(self, arguments):
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self.arguments.append(arguments)
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class ChoiceBuffer:
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def __init__(self, index):
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self.index = index
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self.finish_reason = None
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self.text_content = []
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self.tool_calls_buffers = []
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def append_text_content(self, content):
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self.text_content.append(content)
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def append_tool_call(self, tool_call):
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idx = tool_call.index
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# make sure we have enough tool call buffers
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for _ in range(len(self.tool_calls_buffers), idx + 1):
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self.tool_calls_buffers.append(None)
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if not self.tool_calls_buffers[idx]:
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self.tool_calls_buffers[idx] = ToolCallBuffer(
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idx, tool_call.id, tool_call.function.name
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)
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self.tool_calls_buffers[idx].append_arguments(
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tool_call.function.arguments
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)
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class StreamWrapper:
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span: Span
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response_id: str = ""
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response_model: str = ""
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response_id: Optional[str] = None
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response_model: Optional[str] = None
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service_tier: Optional[str] = None
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finish_reasons: list = []
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prompt_tokens: Optional[int] = 0
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completion_tokens: Optional[int] = 0
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def __init__(
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self,
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stream,
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span,
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prompt_tokens=0,
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function_call=False,
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tool_calls=False,
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stream: Stream,
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span: Span,
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event_logger: EventLogger,
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capture_content: bool,
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):
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self.stream = stream
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self.span = span
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self.prompt_tokens = prompt_tokens
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self.function_call = function_call
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self.tool_calls = tool_calls
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self.result_content = []
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self.completion_tokens = 0
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self.choice_buffers = []
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self._span_started = False
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self.capture_content = capture_content
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self.event_logger = event_logger
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self.setup()
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def setup(self):
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@ -197,16 +220,62 @@ class StreamWrapper:
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GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS,
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self.completion_tokens,
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)
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set_event_completion(
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set_span_attribute(
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self.span,
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[
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{
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"role": "assistant",
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"content": "".join(self.result_content),
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}
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],
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GenAIAttributes.GEN_AI_OPENAI_RESPONSE_SERVICE_TIER,
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self.service_tier,
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)
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set_span_attribute(
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self.span,
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GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS,
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self.finish_reasons,
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)
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for idx, choice in enumerate(self.choice_buffers):
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message = {"role": "assistant"}
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if self.capture_content and choice.text_content:
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message["content"] = "".join(choice.text_content)
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if choice.tool_calls_buffers:
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tool_calls = []
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for tool_call in choice.tool_calls_buffers:
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function = {"name": tool_call.function_name}
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if self.capture_content:
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function["arguments"] = "".join(
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tool_call.arguments
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)
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tool_call_dict = {
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"id": tool_call.tool_call_id,
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"type": "function",
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"function": function,
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}
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tool_calls.append(tool_call_dict)
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message["tool_calls"] = tool_calls
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body = {
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"index": idx,
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"finish_reason": choice.finish_reason or "error",
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"message": message,
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}
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event_attributes = {
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GenAIAttributes.GEN_AI_SYSTEM: GenAIAttributes.GenAiSystemValues.OPENAI.value
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}
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# this span is not current, so we need to manually set the context on event
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span_ctx = self.span.get_span_context()
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self.event_logger.emit(
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Event(
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name="gen_ai.choice",
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attributes=event_attributes,
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body=body,
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trace_id=span_ctx.trace_id,
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span_id=span_ctx.span_id,
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trace_flags=span_ctx.trace_flags,
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)
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)
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self.span.end()
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self._span_started = False
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@ -225,6 +294,10 @@ class StreamWrapper:
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self.cleanup()
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return False # Propagate the exception
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def close(self):
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self.stream.close()
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self.cleanup()
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def __iter__(self):
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return self
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|
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@ -258,50 +331,41 @@ class StreamWrapper:
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if getattr(chunk, "id", None):
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self.response_id = chunk.id
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def set_response_service_tier(self, chunk):
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if self.service_tier:
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return
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if getattr(chunk, "service_tier", None):
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self.service_tier = chunk.service_tier
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def build_streaming_response(self, chunk):
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if getattr(chunk, "choices", None) is None:
|
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return
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choices = chunk.choices
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content = []
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if not self.function_call and not self.tool_calls:
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for choice in choices:
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if choice.delta and choice.delta.content is not None:
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content = [choice.delta.content]
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elif self.function_call:
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for choice in choices:
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if (
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choice.delta
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and choice.delta.function_call is not None
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and choice.delta.function_call.arguments is not None
|
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):
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content = [choice.delta.function_call.arguments]
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elif self.tool_calls:
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for choice in choices:
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if choice.delta and choice.delta.tool_calls is not None:
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toolcalls = choice.delta.tool_calls
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content = []
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for tool_call in toolcalls:
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if (
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tool_call
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and tool_call.function is not None
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and tool_call.function.arguments is not None
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):
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content.append(tool_call.function.arguments)
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finish_reasons = []
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for choice in choices:
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finish_reasons.append(choice.finish_reason or "error")
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if not choice.delta:
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continue
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set_span_attribute(
|
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self.span,
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GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS,
|
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finish_reasons,
|
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)
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if content:
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self.result_content.append(content[0])
|
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# make sure we have enough choice buffers
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for idx in range(len(self.choice_buffers), choice.index + 1):
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self.choice_buffers.append(ChoiceBuffer(idx))
|
||||
|
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if choice.finish_reason:
|
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self.choice_buffers[
|
||||
choice.index
|
||||
].finish_reason = choice.finish_reason
|
||||
|
||||
if choice.delta.content is not None:
|
||||
self.choice_buffers[choice.index].append_text_content(
|
||||
choice.delta.content
|
||||
)
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||||
|
||||
if choice.delta.tool_calls is not None:
|
||||
for tool_call in choice.delta.tool_calls:
|
||||
self.choice_buffers[choice.index].append_tool_call(
|
||||
tool_call
|
||||
)
|
||||
|
||||
def set_usage(self, chunk):
|
||||
if getattr(chunk, "usage", None):
|
||||
|
|
@ -311,5 +375,6 @@ class StreamWrapper:
|
|||
def process_chunk(self, chunk):
|
||||
self.set_response_id(chunk)
|
||||
self.set_response_model(chunk)
|
||||
self.set_response_service_tier(chunk)
|
||||
self.build_streaming_response(chunk)
|
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self.set_usage(chunk)
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|
|
|
|||
|
|
@ -12,100 +12,148 @@
|
|||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import json
|
||||
import logging
|
||||
from os import environ
|
||||
from typing import Optional, Union
|
||||
from urllib.parse import urlparse
|
||||
|
||||
from httpx import URL
|
||||
from openai import NOT_GIVEN
|
||||
|
||||
from opentelemetry._events import Event
|
||||
from opentelemetry.semconv._incubating.attributes import (
|
||||
gen_ai_attributes as GenAIAttributes,
|
||||
)
|
||||
from opentelemetry.semconv._incubating.attributes import (
|
||||
server_attributes as ServerAttributes,
|
||||
)
|
||||
|
||||
OTEL_INSTRUMENTATION_OPENAI_CAPTURE_MESSAGE_CONTENT = (
|
||||
"OTEL_INSTRUMENTATION_OPENAI_CAPTURE_MESSAGE_CONTENT"
|
||||
)
|
||||
|
||||
|
||||
def silently_fail(func):
|
||||
"""
|
||||
A decorator that catches exceptions thrown by the decorated function and logs them as warnings.
|
||||
"""
|
||||
def is_content_enabled() -> bool:
|
||||
capture_content = environ.get(
|
||||
OTEL_INSTRUMENTATION_OPENAI_CAPTURE_MESSAGE_CONTENT, "false"
|
||||
)
|
||||
|
||||
logger = logging.getLogger(func.__module__)
|
||||
|
||||
def wrapper(*args, **kwargs):
|
||||
try:
|
||||
return func(*args, **kwargs)
|
||||
except Exception as exception:
|
||||
logger.warning(
|
||||
"Failed to execute %s, error: %s",
|
||||
func.__name__,
|
||||
str(exception),
|
||||
)
|
||||
|
||||
return wrapper
|
||||
return capture_content.lower() == "true"
|
||||
|
||||
|
||||
def extract_content(choice):
|
||||
if getattr(choice, "message", None) is None:
|
||||
return ""
|
||||
|
||||
# Check if choice.message exists and has a content attribute
|
||||
message = choice.message
|
||||
if getattr(message, "content", None):
|
||||
return choice.message.content
|
||||
|
||||
# Check if choice.message has tool_calls and extract information accordingly
|
||||
elif getattr(message, "tool_calls", None):
|
||||
result = [
|
||||
{
|
||||
"id": tool_call.id,
|
||||
"type": tool_call.type,
|
||||
"function": {
|
||||
"name": tool_call.function.name,
|
||||
"arguments": tool_call.function.arguments,
|
||||
},
|
||||
}
|
||||
for tool_call in choice.message.tool_calls
|
||||
]
|
||||
return result
|
||||
|
||||
# Check if choice.message has a function_call and extract information accordingly
|
||||
elif getattr(message, "function_call", None):
|
||||
return {
|
||||
"name": choice.message.function_call.name,
|
||||
"arguments": choice.message.function_call.arguments,
|
||||
}
|
||||
|
||||
# Return an empty string if none of the above conditions are met
|
||||
else:
|
||||
return ""
|
||||
|
||||
|
||||
def extract_tools_prompt(item):
|
||||
tool_calls = getattr(item, "tool_calls", None)
|
||||
def extract_tool_calls(item, capture_content):
|
||||
tool_calls = get_property_value(item, "tool_calls")
|
||||
if tool_calls is None:
|
||||
return
|
||||
return None
|
||||
|
||||
calls = []
|
||||
for tool_call in tool_calls:
|
||||
tool_call_dict = {
|
||||
"id": getattr(tool_call, "id", ""),
|
||||
"type": getattr(tool_call, "type", ""),
|
||||
}
|
||||
tool_call_dict = {}
|
||||
call_id = get_property_value(tool_call, "id")
|
||||
if call_id:
|
||||
tool_call_dict["id"] = call_id
|
||||
|
||||
tool_type = get_property_value(tool_call, "type")
|
||||
if tool_type:
|
||||
tool_call_dict["type"] = tool_type
|
||||
|
||||
func = get_property_value(tool_call, "function")
|
||||
if func:
|
||||
tool_call_dict["function"] = {}
|
||||
|
||||
name = get_property_value(func, "name")
|
||||
if name:
|
||||
tool_call_dict["function"]["name"] = name
|
||||
|
||||
arguments = get_property_value(func, "arguments")
|
||||
if capture_content and arguments:
|
||||
if isinstance(arguments, str):
|
||||
arguments = arguments.replace("\n", "")
|
||||
tool_call_dict["function"]["arguments"] = arguments
|
||||
|
||||
if hasattr(tool_call, "function"):
|
||||
tool_call_dict["function"] = {
|
||||
"name": getattr(tool_call.function, "name", ""),
|
||||
"arguments": getattr(tool_call.function, "arguments", ""),
|
||||
}
|
||||
calls.append(tool_call_dict)
|
||||
return calls
|
||||
|
||||
|
||||
def set_event_prompt(span, prompt):
|
||||
span.add_event(
|
||||
name="gen_ai.content.prompt",
|
||||
attributes={
|
||||
GenAIAttributes.GEN_AI_PROMPT: prompt,
|
||||
},
|
||||
def set_server_address_and_port(client_instance, attributes):
|
||||
base_client = getattr(client_instance, "_client", None)
|
||||
base_url = getattr(base_client, "base_url", None)
|
||||
if not base_url:
|
||||
return
|
||||
|
||||
port = -1
|
||||
if isinstance(base_url, URL):
|
||||
attributes[ServerAttributes.SERVER_ADDRESS] = base_url.host
|
||||
port = base_url.port
|
||||
elif isinstance(base_url, str):
|
||||
url = urlparse(base_url)
|
||||
attributes[ServerAttributes.SERVER_ADDRESS] = url.hostname
|
||||
port = url.port
|
||||
|
||||
if port and port != 443 and port > 0:
|
||||
attributes[ServerAttributes.SERVER_PORT] = port
|
||||
|
||||
|
||||
def get_property_value(obj, property_name):
|
||||
if isinstance(obj, dict):
|
||||
return obj.get(property_name, None)
|
||||
|
||||
return getattr(obj, property_name, None)
|
||||
|
||||
|
||||
def message_to_event(message, capture_content):
|
||||
attributes = {
|
||||
GenAIAttributes.GEN_AI_SYSTEM: GenAIAttributes.GenAiSystemValues.OPENAI.value
|
||||
}
|
||||
role = get_property_value(message, "role")
|
||||
content = get_property_value(message, "content")
|
||||
|
||||
body = {}
|
||||
if capture_content and content:
|
||||
body["content"] = content
|
||||
if role == "assistant":
|
||||
tool_calls = extract_tool_calls(message, capture_content)
|
||||
if tool_calls:
|
||||
body = {"tool_calls": tool_calls}
|
||||
elif role == "tool":
|
||||
tool_call_id = get_property_value(message, "tool_call_id")
|
||||
if tool_call_id:
|
||||
body["id"] = tool_call_id
|
||||
|
||||
return Event(
|
||||
name=f"gen_ai.{role}.message",
|
||||
attributes=attributes,
|
||||
body=body if body else None,
|
||||
)
|
||||
|
||||
|
||||
def choice_to_event(choice, capture_content):
|
||||
attributes = {
|
||||
GenAIAttributes.GEN_AI_SYSTEM: GenAIAttributes.GenAiSystemValues.OPENAI.value
|
||||
}
|
||||
|
||||
body = {
|
||||
"index": choice.index,
|
||||
"finish_reason": choice.finish_reason or "error",
|
||||
}
|
||||
|
||||
if choice.message:
|
||||
message = {
|
||||
"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:
|
||||
message["tool_calls"] = tool_calls
|
||||
content = get_property_value(choice.message, "content")
|
||||
if capture_content and content:
|
||||
message["content"] = content
|
||||
body["message"] = message
|
||||
|
||||
return Event(
|
||||
name="gen_ai.choice",
|
||||
attributes=attributes,
|
||||
body=body,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -114,15 +162,6 @@ def set_span_attributes(span, attributes: dict):
|
|||
set_span_attribute(span, field, value)
|
||||
|
||||
|
||||
def set_event_completion(span, result_content):
|
||||
span.add_event(
|
||||
name="gen_ai.content.completion",
|
||||
attributes={
|
||||
GenAIAttributes.GEN_AI_COMPLETION: json.dumps(result_content),
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
def set_span_attribute(span, name, value):
|
||||
if non_numerical_value_is_set(value) is False:
|
||||
return
|
||||
|
|
@ -140,14 +179,13 @@ def non_numerical_value_is_set(value: Optional[Union[bool, str]]):
|
|||
|
||||
def get_llm_request_attributes(
|
||||
kwargs,
|
||||
client_instance,
|
||||
operation_name=GenAIAttributes.GenAiOperationNameValues.CHAT.value,
|
||||
):
|
||||
attributes = {
|
||||
GenAIAttributes.GEN_AI_OPERATION_NAME: operation_name,
|
||||
GenAIAttributes.GEN_AI_SYSTEM: GenAIAttributes.GenAiSystemValues.OPENAI.value,
|
||||
GenAIAttributes.GEN_AI_REQUEST_MODEL: kwargs.get(
|
||||
"model", "gpt-3.5-turbo"
|
||||
),
|
||||
GenAIAttributes.GEN_AI_REQUEST_MODEL: kwargs.get("model"),
|
||||
GenAIAttributes.GEN_AI_REQUEST_TEMPERATURE: kwargs.get("temperature"),
|
||||
GenAIAttributes.GEN_AI_REQUEST_TOP_P: kwargs.get("p")
|
||||
or kwargs.get("top_p"),
|
||||
|
|
@ -158,7 +196,17 @@ def get_llm_request_attributes(
|
|||
GenAIAttributes.GEN_AI_REQUEST_FREQUENCY_PENALTY: kwargs.get(
|
||||
"frequency_penalty"
|
||||
),
|
||||
GenAIAttributes.GEN_AI_OPENAI_REQUEST_RESPONSE_FORMAT: kwargs.get(
|
||||
"response_format"
|
||||
),
|
||||
GenAIAttributes.GEN_AI_OPENAI_REQUEST_SEED: kwargs.get("seed"),
|
||||
}
|
||||
|
||||
set_server_address_and_port(client_instance, attributes)
|
||||
service_tier = kwargs.get("service_tier")
|
||||
attributes[GenAIAttributes.GEN_AI_OPENAI_RESPONSE_SERVICE_TIER] = (
|
||||
service_tier if service_tier != "auto" else None
|
||||
)
|
||||
|
||||
# filter out None values
|
||||
return {k: v for k, v in attributes.items() if v is not None}
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
openai==1.26.0
|
||||
openai==1.26.0
|
||||
pydantic==2.8.2
|
||||
Deprecated==1.2.14
|
||||
importlib-metadata==6.11.0
|
||||
|
|
|
|||
|
|
@ -0,0 +1,75 @@
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interactions:
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- request:
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body: '{"messages": [{"role": "user", "content": "Say this is a test"}], "model":
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version: 1
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|
@ -5,8 +5,16 @@ import os
|
|||
import pytest
|
||||
from openai import OpenAI
|
||||
|
||||
from opentelemetry import trace
|
||||
from opentelemetry.instrumentation.openai_v2 import OpenAIInstrumentor
|
||||
from opentelemetry.instrumentation.openai_v2.utils import (
|
||||
OTEL_INSTRUMENTATION_OPENAI_CAPTURE_MESSAGE_CONTENT,
|
||||
)
|
||||
from opentelemetry.sdk._events import EventLoggerProvider
|
||||
from opentelemetry.sdk._logs import LoggerProvider
|
||||
from opentelemetry.sdk._logs.export import (
|
||||
InMemoryLogExporter,
|
||||
SimpleLogRecordProcessor,
|
||||
)
|
||||
from opentelemetry.sdk.trace import TracerProvider
|
||||
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
|
||||
from opentelemetry.sdk.trace.export.in_memory_span_exporter import (
|
||||
|
|
@ -14,21 +22,32 @@ from opentelemetry.sdk.trace.export.in_memory_span_exporter import (
|
|||
)
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def exporter():
|
||||
@pytest.fixture(scope="function", name="span_exporter")
|
||||
def fixture_span_exporter():
|
||||
exporter = InMemorySpanExporter()
|
||||
processor = SimpleSpanProcessor(exporter)
|
||||
yield exporter
|
||||
|
||||
|
||||
@pytest.fixture(scope="function", name="log_exporter")
|
||||
def fixture_log_exporter():
|
||||
exporter = InMemoryLogExporter()
|
||||
yield exporter
|
||||
|
||||
|
||||
@pytest.fixture(scope="function", name="tracer_provider")
|
||||
def fixture_tracer_provider(span_exporter):
|
||||
provider = TracerProvider()
|
||||
provider.add_span_processor(processor)
|
||||
trace.set_tracer_provider(provider)
|
||||
|
||||
return exporter
|
||||
provider.add_span_processor(SimpleSpanProcessor(span_exporter))
|
||||
return provider
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def clear_exporter(exporter):
|
||||
exporter.clear()
|
||||
@pytest.fixture(scope="function", name="event_logger_provider")
|
||||
def fixture_event_logger_provider(log_exporter):
|
||||
provider = LoggerProvider()
|
||||
provider.add_log_record_processor(SimpleLogRecordProcessor(log_exporter))
|
||||
event_logger_provider = EventLoggerProvider(provider)
|
||||
|
||||
return event_logger_provider
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
|
|
@ -51,14 +70,32 @@ def vcr_config():
|
|||
}
|
||||
|
||||
|
||||
@pytest.fixture(scope="session", autouse=True)
|
||||
def instrument():
|
||||
OpenAIInstrumentor().instrument()
|
||||
@pytest.fixture(scope="function")
|
||||
def instrument_no_content(tracer_provider, event_logger_provider):
|
||||
instrumentor = OpenAIInstrumentor()
|
||||
instrumentor.instrument(
|
||||
tracer_provider=tracer_provider,
|
||||
event_logger_provider=event_logger_provider,
|
||||
)
|
||||
|
||||
yield instrumentor
|
||||
instrumentor.uninstrument()
|
||||
|
||||
|
||||
@pytest.fixture(scope="session", autouse=True)
|
||||
def uninstrument():
|
||||
OpenAIInstrumentor().uninstrument()
|
||||
@pytest.fixture(scope="function")
|
||||
def instrument_with_content(tracer_provider, event_logger_provider):
|
||||
os.environ.update(
|
||||
{OTEL_INSTRUMENTATION_OPENAI_CAPTURE_MESSAGE_CONTENT: "True"}
|
||||
)
|
||||
instrumentor = OpenAIInstrumentor()
|
||||
instrumentor.instrument(
|
||||
tracer_provider=tracer_provider,
|
||||
event_logger_provider=event_logger_provider,
|
||||
)
|
||||
|
||||
yield instrumentor
|
||||
os.environ.pop(OTEL_INSTRUMENTATION_OPENAI_CAPTURE_MESSAGE_CONTENT, None)
|
||||
instrumentor.uninstrument()
|
||||
|
||||
|
||||
def scrub_response_headers(response):
|
||||
|
|
|
|||
|
|
@ -1,82 +1,390 @@
|
|||
import json
|
||||
# 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 OpenAI
|
||||
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()
|
||||
def test_chat_completion(exporter, openai_client):
|
||||
llm_model_value = "gpt-4"
|
||||
def test_chat_completion_with_content(
|
||||
span_exporter, log_exporter, openai_client, instrument_with_content
|
||||
):
|
||||
llm_model_value = "gpt-4o-mini"
|
||||
messages_value = [{"role": "user", "content": "Say this is a test"}]
|
||||
|
||||
kwargs = {
|
||||
"model": llm_model_value,
|
||||
"messages": messages_value,
|
||||
"stream": False,
|
||||
response = 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])
|
||||
|
||||
response = openai_client.chat.completions.create(**kwargs)
|
||||
spans = exporter.get_finished_spans()
|
||||
chat_completion_span = spans[0]
|
||||
# assert that the span name is correct
|
||||
assert chat_completion_span.name == f"chat {llm_model_value}"
|
||||
|
||||
attributes = chat_completion_span.attributes
|
||||
operation_name = attributes[GenAIAttributes.GEN_AI_OPERATION_NAME]
|
||||
system = attributes[GenAIAttributes.GEN_AI_SYSTEM]
|
||||
request_model = attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL]
|
||||
response_model = attributes[GenAIAttributes.GEN_AI_RESPONSE_MODEL]
|
||||
response_id = attributes[GenAIAttributes.GEN_AI_RESPONSE_ID]
|
||||
input_tokens = attributes[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS]
|
||||
output_tokens = attributes[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS]
|
||||
# assert that the attributes are correct
|
||||
assert (
|
||||
operation_name == GenAIAttributes.GenAiOperationNameValues.CHAT.value
|
||||
)
|
||||
assert system == GenAIAttributes.GenAiSystemValues.OPENAI.value
|
||||
assert request_model == llm_model_value
|
||||
assert response_model == response.model
|
||||
assert response_id == response.id
|
||||
assert input_tokens == response.usage.prompt_tokens
|
||||
assert output_tokens == response.usage.completion_tokens
|
||||
def test_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"}]
|
||||
|
||||
events = chat_completion_span.events
|
||||
client = OpenAI(base_url="http://localhost:4242")
|
||||
|
||||
# assert that the prompt and completion events are present
|
||||
prompt_event = list(
|
||||
filter(
|
||||
lambda event: event.name == "gen_ai.content.prompt",
|
||||
events,
|
||||
exception = None
|
||||
try:
|
||||
client.chat.completions.create(
|
||||
messages=messages_value,
|
||||
model=llm_model_value,
|
||||
timeout=0.1,
|
||||
)
|
||||
assert False, "Expected an exception"
|
||||
except Exception as ex: # pylint: disable=broad-exception-caught
|
||||
exception = ex
|
||||
|
||||
spans = span_exporter.get_finished_spans()
|
||||
assert_all_attributes(
|
||||
spans[0], llm_model_value, server_address="localhost"
|
||||
)
|
||||
completion_event = list(
|
||||
filter(
|
||||
lambda event: event.name == "gen_ai.content.completion",
|
||||
events,
|
||||
)
|
||||
)
|
||||
|
||||
assert prompt_event
|
||||
assert completion_event
|
||||
|
||||
# assert that the prompt and completion events have the correct attributes
|
||||
assert prompt_event[0].attributes[
|
||||
GenAIAttributes.GEN_AI_PROMPT
|
||||
] == json.dumps(messages_value)
|
||||
|
||||
assert 4242 == spans[0].attributes[ServerAttributes.SERVER_PORT]
|
||||
assert (
|
||||
json.loads(
|
||||
completion_event[0].attributes[GenAIAttributes.GEN_AI_COMPLETION]
|
||||
)[0]["content"]
|
||||
== response.choices[0].message.content
|
||||
type(exception).__qualname__
|
||||
== spans[0].attributes[ErrorAttributes.ERROR_TYPE]
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_chat_completion_streaming(exporter, openai_client):
|
||||
def test_chat_completion_404(
|
||||
span_exporter, openai_client, instrument_no_content
|
||||
):
|
||||
llm_model_value = "this-model-does-not-exist"
|
||||
messages_value = [{"role": "user", "content": "Say this is a test"}]
|
||||
|
||||
exception = None
|
||||
try:
|
||||
openai_client.chat.completions.create(
|
||||
messages=messages_value,
|
||||
model=llm_model_value,
|
||||
timeout=0.1,
|
||||
)
|
||||
assert False, "Expected an exception"
|
||||
except Exception as ex: # pylint: disable=broad-exception-caught
|
||||
exception = ex
|
||||
|
||||
spans = span_exporter.get_finished_spans()
|
||||
|
||||
assert_all_attributes(spans[0], llm_model_value)
|
||||
assert (
|
||||
type(exception).__qualname__
|
||||
== spans[0].attributes[ErrorAttributes.ERROR_TYPE]
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_chat_completion_extra_params(
|
||||
span_exporter, openai_client, instrument_no_content
|
||||
):
|
||||
llm_model_value = "gpt-4o-mini"
|
||||
messages_value = [{"role": "user", "content": "Say this is a test"}]
|
||||
|
||||
response = 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()
|
||||
def test_chat_completion_multiple_choices(
|
||||
span_exporter, log_exporter, openai_client, instrument_with_content
|
||||
):
|
||||
llm_model_value = "gpt-4o-mini"
|
||||
messages_value = [{"role": "user", "content": "Say this is a test"}]
|
||||
|
||||
response = 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()
|
||||
def test_chat_completion_tool_calls_with_content(
|
||||
span_exporter, log_exporter, openai_client, instrument_with_content
|
||||
):
|
||||
chat_completion_tool_call(span_exporter, log_exporter, openai_client, True)
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_chat_completion_tool_calls_no_content(
|
||||
span_exporter, log_exporter, openai_client, instrument_no_content
|
||||
):
|
||||
chat_completion_tool_call(
|
||||
span_exporter, log_exporter, openai_client, False
|
||||
)
|
||||
|
||||
|
||||
def chat_completion_tool_call(
|
||||
span_exporter, log_exporter, 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 = 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 = 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()
|
||||
def test_chat_completion_streaming(
|
||||
span_exporter, log_exporter, openai_client, instrument_with_content
|
||||
):
|
||||
llm_model_value = "gpt-4"
|
||||
messages_value = [{"role": "user", "content": "Say this is a test"}]
|
||||
|
||||
|
|
@ -102,56 +410,428 @@ def test_chat_completion_streaming(exporter, openai_client):
|
|||
response_stream_model = chunk.model
|
||||
response_stream_id = chunk.id
|
||||
|
||||
spans = exporter.get_finished_spans()
|
||||
streaming_span = spans[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,
|
||||
)
|
||||
|
||||
assert streaming_span.name == f"chat {llm_model_value}"
|
||||
attributes = streaming_span.attributes
|
||||
logs = log_exporter.get_finished_logs()
|
||||
assert len(logs) == 2
|
||||
|
||||
operation_name = attributes[GenAIAttributes.GEN_AI_OPERATION_NAME]
|
||||
system = attributes[GenAIAttributes.GEN_AI_SYSTEM]
|
||||
request_model = attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL]
|
||||
response_model = attributes[GenAIAttributes.GEN_AI_RESPONSE_MODEL]
|
||||
response_id = attributes[GenAIAttributes.GEN_AI_RESPONSE_ID]
|
||||
input_tokens = attributes[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS]
|
||||
output_tokens = attributes[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS]
|
||||
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()
|
||||
def test_chat_completion_streaming_not_complete(
|
||||
span_exporter, log_exporter, 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 = openai_client.chat.completions.create(**kwargs)
|
||||
for idx, chunk in enumerate(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
|
||||
|
||||
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()
|
||||
def test_chat_completion_multiple_choices_streaming(
|
||||
span_exporter, log_exporter, 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 = 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 = ["", ""]
|
||||
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()
|
||||
def test_chat_completion_multiple_tools_streaming_with_content(
|
||||
span_exporter, log_exporter, openai_client, instrument_with_content
|
||||
):
|
||||
chat_completion_multiple_tools_streaming(
|
||||
span_exporter, log_exporter, openai_client, True
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_chat_completion_multiple_tools_streaming_no_content(
|
||||
span_exporter, log_exporter, openai_client, instrument_no_content
|
||||
):
|
||||
chat_completion_multiple_tools_streaming(
|
||||
span_exporter, log_exporter, openai_client, False
|
||||
)
|
||||
|
||||
|
||||
def chat_completion_multiple_tools_streaming(
|
||||
span_exporter, log_exporter, 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 = 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 = ["", ""]
|
||||
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 (
|
||||
operation_name == GenAIAttributes.GenAiOperationNameValues.CHAT.value
|
||||
log.log_record.attributes[GenAIAttributes.GEN_AI_SYSTEM]
|
||||
== GenAIAttributes.GenAiSystemValues.OPENAI.value
|
||||
)
|
||||
assert system == GenAIAttributes.GenAiSystemValues.OPENAI.value
|
||||
assert request_model == llm_model_value
|
||||
assert response_model == response_stream_model
|
||||
assert response_id == response_stream_id
|
||||
assert input_tokens == response_stream_usage.prompt_tokens
|
||||
assert output_tokens == response_stream_usage.completion_tokens
|
||||
|
||||
events = streaming_span.events
|
||||
|
||||
# assert that the prompt and completion events are present
|
||||
prompt_event = list(
|
||||
filter(
|
||||
lambda event: event.name == "gen_ai.content.prompt",
|
||||
events,
|
||||
)
|
||||
)
|
||||
completion_event = list(
|
||||
filter(
|
||||
lambda event: event.name == "gen_ai.content.completion",
|
||||
events,
|
||||
if not expected_content:
|
||||
assert not log.log_record.body
|
||||
else:
|
||||
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,
|
||||
)
|
||||
|
||||
assert prompt_event
|
||||
assert completion_event
|
||||
|
||||
# assert that the prompt and completion events have the correct attributes
|
||||
assert prompt_event[0].attributes[
|
||||
GenAIAttributes.GEN_AI_PROMPT
|
||||
] == json.dumps(messages_value)
|
||||
|
||||
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 (
|
||||
json.loads(
|
||||
completion_event[0].attributes[GenAIAttributes.GEN_AI_COMPLETION]
|
||||
)[0]["content"]
|
||||
== response_stream_result
|
||||
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,
|
||||
},
|
||||
},
|
||||
}
|
||||
|
|
|
|||
3
tox.ini
3
tox.ini
|
|
@ -910,6 +910,9 @@ commands =
|
|||
test-instrumentation-mysqlclient: pytest {toxinidir}/instrumentation/opentelemetry-instrumentation-mysqlclient/tests {posargs}
|
||||
lint-instrumentation-mysqlclient: sh -c "cd instrumentation && pylint --rcfile ../.pylintrc opentelemetry-instrumentation-mysqlclient"
|
||||
|
||||
test-instrumentation-openai-v2: pytest {toxinidir}/instrumentation/opentelemetry-instrumentation-openai-v2/tests {posargs}
|
||||
lint-instrumentation-openai-v2: sh -c "cd instrumentation && pylint --rcfile ../.pylintrc opentelemetry-instrumentation-openai-v2"
|
||||
|
||||
test-instrumentation-sio-pika: pytest {toxinidir}/instrumentation/opentelemetry-instrumentation-pika/tests {posargs}
|
||||
lint-instrumentation-sio-pika: sh -c "cd instrumentation && pylint --rcfile ../.pylintrc opentelemetry-instrumentation-pika"
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue