diff --git a/instrumentation-genai/__init__.py b/instrumentation-genai/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/CHANGELOG.md b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/CHANGELOG.md new file mode 100644 index 0000000..1329cee --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/CHANGELOG.md @@ -0,0 +1,44 @@ +# Changelog + +All notable changes to this project will be documented in this file. + +The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), +and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). + +## Unreleased + +## Version 2.2b0 (2025-11-25) + +- Fix service tier attribute names: use `GEN_AI_OPENAI_REQUEST_SERVICE_TIER` for request + attributes and `GEN_AI_OPENAI_RESPONSE_SERVICE_TIER` for response attributes. + ([#3920](https://github.com/open-telemetry/opentelemetry-python-contrib/issues/3920)) +- Added support for OpenAI embeddings instrumentation + ([#3461](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/3461)) +- Record prompt and completion events regardless of span sampling decision. + ([#3226](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/3226)) +- Filter out attributes with the value of NotGiven instances + ([#3760](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/3760)) +- Migrate off the deprecated events API to use the logs API + ([#3625](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/3628)) + +## Version 2.1b0 (2025-01-18) + +- Coerce openai response_format to semconv format + ([#3073](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/3073)) +- Add example to `opentelemetry-instrumentation-openai-v2` + ([#3006](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/3006)) +- Support for `AsyncOpenAI/AsyncCompletions` ([#2984](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/2984)) +- Add metrics ([#3180](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/3180)) + +## Version 2.0b0 (2024-11-08) + +- Use generic `OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT` environment variable + to control if content of prompt, completion, and other messages is captured. + ([#2947](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/2947)) + +- Update OpenAI instrumentation to Semantic Conventions v1.28.0: add new attributes + and switch prompts and completions to log-based events. + ([#2925](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/2925)) + +- Initial OpenAI instrumentation + ([#2759](https://github.com/open-telemetry/opentelemetry-python-contrib/pull/2759)) diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/LICENSE b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/LICENSE new file mode 100644 index 0000000..261eeb9 --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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It also captures +the duration of the operations and the number of tokens used as metrics. + +Many LLM platforms support the OpenAI SDK. This means systems such as the following are observable with this instrumentation when accessed using it: + +.. list-table:: OpenAI Compatible Platforms + :widths: 40 25 + :header-rows: 1 + + * - Name + - gen_ai.system + * - `Azure OpenAI `_ + - ``az.ai.openai`` + * - `Gemini `_ + - ``gemini`` + * - `Perplexity `_ + - ``perplexity`` + * - `xAI `_ (Compatible with Anthropic) + - ``xai`` + * - `DeepSeek `_ + - ``deepseek`` + * - `Groq `_ + - ``groq`` + * - `MistralAI `_ + - ``mistral_ai`` + +Installation +------------ + +If your application is already instrumented with OpenTelemetry, add this +package to your requirements. +:: + + pip install opentelemetry-instrumentation-openai-v2 + +If you don't have an OpenAI application, yet, try our `examples `_ +which only need a valid OpenAI API key. + +Check out `zero-code example `_ for a quick start. + +Usage +----- + +This section describes how to set up OpenAI instrumentation if you're setting OpenTelemetry up manually. +Check out the `manual example `_ for more details. + +Instrumenting all clients +************************* + +When using the instrumentor, all clients will automatically trace OpenAI operations including chat completions and embeddings. +You can also optionally capture prompts and completions as log events. + +Make sure to configure OpenTelemetry tracing, logging, and events to capture all telemetry emitted by the instrumentation. + +.. code-block:: python + + from opentelemetry.instrumentation.openai_v2 import OpenAIInstrumentor + + OpenAIInstrumentor().instrument() + + client = OpenAI() + # Chat completion example + response = client.chat.completions.create( + model="gpt-4o-mini", + messages=[ + {"role": "user", "content": "Write a short poem on open telemetry."}, + ], + ) + + # Embeddings example + embedding_response = client.embeddings.create( + model="text-embedding-3-small", + input="Generate vector embeddings for this text" + ) + +Enabling message content +************************* + +Message content such as the contents of the prompt, completion, function arguments and return values +are not captured by default. To capture message content as log events, set the environment variable +`OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT` to `true`. + +Uninstrument +************ + +To uninstrument clients, call the uninstrument method: + +.. code-block:: python + + from opentelemetry.instrumentation.openai_v2 import OpenAIInstrumentor + + OpenAIInstrumentor().instrument() + # ... + + # Uninstrument all clients + OpenAIInstrumentor().uninstrument() + +References +---------- +* `OpenTelemetry OpenAI Instrumentation `_ +* `OpenTelemetry Project `_ +* `OpenTelemetry Python Examples `_ + diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/__init__.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/__init__.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/embeddings/.env b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/embeddings/.env new file mode 100644 index 0000000..8f2dd62 --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/embeddings/.env @@ -0,0 +1,22 @@ +# Update this with your real OpenAI API key +OPENAI_API_KEY=sk-YOUR_API_KEY + +# Uncomment to use Ollama instead of OpenAI +# OPENAI_BASE_URL=http://localhost:11434/v1 +# OPENAI_API_KEY=unused +# CHAT_MODEL=qwen2.5:0.5b + +# Uncomment and change to your OTLP endpoint +# OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317 +# OTEL_EXPORTER_OTLP_PROTOCOL=grpc + +OTEL_SERVICE_NAME=opentelemetry-python-openai + +# Change to 'false' to disable collection of python logging logs +OTEL_PYTHON_LOGGING_AUTO_INSTRUMENTATION_ENABLED=true + +# Uncomment if your OTLP endpoint doesn't support logs +# OTEL_LOGS_EXPORTER=console + +# Change to 'false' to hide prompt and completion content +OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=true diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/embeddings/README.rst b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/embeddings/README.rst new file mode 100644 index 0000000..66a7c58 --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/embeddings/README.rst @@ -0,0 +1,44 @@ +OpenTelemetry OpenAI Embeddings API Instrumentation Example +=========================================================== + +This is an example of how to instrument OpenAI Embeddings API calls with zero code changes, +using ``opentelemetry-instrument``. + +When ``main.py`` is run, it exports traces and metrics to an OTLP +compatible endpoint. Traces include details such as the model used, +dimensions of embeddings, and the duration of the embedding request. +Metrics capture token usage and performance data. + +Note: ``.env`` file configures additional environment variables: + +- ``OTEL_PYTHON_LOGGING_AUTO_INSTRUMENTATION_ENABLED=true`` configures OpenTelemetry SDK to export logs and events. +- ``OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=true`` configures OpenAI instrumentation to capture content on events. +- ``OTEL_LOGS_EXPORTER=otlp`` to specify exporter type. + +Setup +----- + +Minimally, update the ``.env`` file with your ``OPENAI_API_KEY``. An +OTLP compatible endpoint should be listening for traces and logs on +http://localhost:4317. If not, update ``OTEL_EXPORTER_OTLP_ENDPOINT`` as well. + +Next, set up a virtual environment like this: + +:: + + python3 -m venv .venv + source .venv/bin/activate + pip install "python-dotenv[cli]" + pip install -r requirements.txt + +Run +--- + +Run the example like this: + +:: + + dotenv run -- opentelemetry-instrument python main.py + +You should see embedding information printed while traces and metrics export to your +configured observability tool. \ No newline at end of file diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/embeddings/__init__.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/embeddings/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/embeddings/main.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/embeddings/main.py new file mode 100644 index 0000000..7eddc0a --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/embeddings/main.py @@ -0,0 +1,29 @@ +import os + +from openai import OpenAI + + +def main(): + client = OpenAI() + + # Create embeddings with OpenAI API + embedding_response = client.embeddings.create( + model=os.getenv("EMBEDDING_MODEL", "text-embedding-3-small"), + input="OpenTelemetry provides observability for your applications.", + ) + + # Print embedding information + print(f"Model: {embedding_response.model}") + print(f"Dimensions: {len(embedding_response.data[0].embedding)}") + print( + f"Token usage - Prompt: {embedding_response.usage.prompt_tokens}, Total: {embedding_response.usage.total_tokens}" + ) + + # Print a sample of the embedding vector (first 5 dimensions) + print( + f"Embedding sample (first 5 dimensions): {embedding_response.data[0].embedding[:5]}" + ) + + +if __name__ == "__main__": + main() diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/embeddings/requirements.txt b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/embeddings/requirements.txt new file mode 100644 index 0000000..5b6bbd3 --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/embeddings/requirements.txt @@ -0,0 +1,6 @@ +openai~=1.57.3 + +opentelemetry-sdk~=1.36.0 +opentelemetry-exporter-otlp-proto-grpc~=1.36.0 +opentelemetry-distro~=0.57b0 +opentelemetry-instrumentation-openai-v2~=2.2b0 diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/manual/.env b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/manual/.env new file mode 100644 index 0000000..1e77ee7 --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/manual/.env @@ -0,0 +1,16 @@ +# Update this with your real OpenAI API key +OPENAI_API_KEY=sk-YOUR_API_KEY + +# Uncomment to use Ollama instead of OpenAI +# OPENAI_BASE_URL=http://localhost:11434/v1 +# OPENAI_API_KEY=unused +# CHAT_MODEL=qwen2.5:0.5b + +# Uncomment and change to your OTLP endpoint +# OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317 +# OTEL_EXPORTER_OTLP_PROTOCOL=grpc + +OTEL_SERVICE_NAME=opentelemetry-python-openai + +# Change to 'false' to hide prompt and completion content +OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=true diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/manual/README.rst b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/manual/README.rst new file mode 100644 index 0000000..61e4c4a --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/manual/README.rst @@ -0,0 +1,42 @@ +OpenTelemetry OpenAI Instrumentation Example +============================================ + +This is an example of how to instrument OpenAI calls when configuring OpenTelemetry SDK and Instrumentations manually. + +When `main.py `_ is run, it exports traces and logs to an OTLP +compatible endpoint. Traces include details such as the model used and the +duration of the chat request. Logs capture the chat request and the generated +response, providing a comprehensive view of the performance and behavior of +your OpenAI requests. + +Note: `.env <.env>`_ file configures additional environment variables: + +- ``OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=true`` configures OpenAI instrumentation to capture prompt and completion contents on events. + +Setup +----- + +Minimally, update the `.env <.env>`_ file with your ``OPENAI_API_KEY``. An +OTLP compatible endpoint should be listening for traces and logs on +http://localhost:4317. If not, update ``OTEL_EXPORTER_OTLP_ENDPOINT`` as well. + +Next, set up a virtual environment like this: + +:: + + python3 -m venv .venv + source .venv/bin/activate + pip install "python-dotenv[cli]" + pip install -r requirements.txt + +Run +--- + +Run the example like this: + +:: + + dotenv run -- python main.py + +You should see a poem generated by OpenAI while traces and logs export to your +configured observability tool. diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/manual/__init__.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/manual/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/manual/main.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/manual/main.py new file mode 100644 index 0000000..815a98a --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/manual/main.py @@ -0,0 +1,67 @@ +# pylint: skip-file +import os + +from openai import OpenAI + +# NOTE: OpenTelemetry Python Logs API is in beta +from opentelemetry import _logs, metrics, trace +from opentelemetry.exporter.otlp.proto.grpc._log_exporter import ( + OTLPLogExporter, +) +from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import ( + OTLPMetricExporter, +) +from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import ( + OTLPSpanExporter, +) +from opentelemetry.instrumentation.openai_v2 import OpenAIInstrumentor +from opentelemetry.sdk._logs import LoggerProvider +from opentelemetry.sdk._logs.export import BatchLogRecordProcessor +from opentelemetry.sdk.metrics import MeterProvider +from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader +from opentelemetry.sdk.trace import TracerProvider +from opentelemetry.sdk.trace.export import BatchSpanProcessor + +# configure tracing +trace.set_tracer_provider(TracerProvider()) +trace.get_tracer_provider().add_span_processor( + BatchSpanProcessor(OTLPSpanExporter()) +) + +# configure logging +_logs.set_logger_provider(LoggerProvider()) +_logs.get_logger_provider().add_log_record_processor( + BatchLogRecordProcessor(OTLPLogExporter()) +) + +# configure metrics +metrics.set_meter_provider( + MeterProvider( + metric_readers=[ + PeriodicExportingMetricReader( + OTLPMetricExporter(), + ), + ] + ) +) + +# instrument OpenAI +OpenAIInstrumentor().instrument() + + +def main(): + client = OpenAI() + chat_completion = client.chat.completions.create( + model=os.getenv("CHAT_MODEL", "gpt-4o-mini"), + messages=[ + { + "role": "user", + "content": "Write a short poem on OpenTelemetry.", + }, + ], + ) + print(chat_completion.choices[0].message.content) + + +if __name__ == "__main__": + main() diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/manual/requirements.txt b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/manual/requirements.txt new file mode 100644 index 0000000..4bde8de --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/manual/requirements.txt @@ -0,0 +1,5 @@ +openai~=1.57.3 + +opentelemetry-sdk~=1.36.0 +opentelemetry-exporter-otlp-proto-grpc~=1.36.0 +opentelemetry-instrumentation-openai-v2~=2.1b0 diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/zero-code/.env b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/zero-code/.env new file mode 100644 index 0000000..8f2dd62 --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/zero-code/.env @@ -0,0 +1,22 @@ +# Update this with your real OpenAI API key +OPENAI_API_KEY=sk-YOUR_API_KEY + +# Uncomment to use Ollama instead of OpenAI +# OPENAI_BASE_URL=http://localhost:11434/v1 +# OPENAI_API_KEY=unused +# CHAT_MODEL=qwen2.5:0.5b + +# Uncomment and change to your OTLP endpoint +# OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317 +# OTEL_EXPORTER_OTLP_PROTOCOL=grpc + +OTEL_SERVICE_NAME=opentelemetry-python-openai + +# Change to 'false' to disable collection of python logging logs +OTEL_PYTHON_LOGGING_AUTO_INSTRUMENTATION_ENABLED=true + +# Uncomment if your OTLP endpoint doesn't support logs +# OTEL_LOGS_EXPORTER=console + +# Change to 'false' to hide prompt and completion content +OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=true diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/zero-code/README.rst b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/zero-code/README.rst new file mode 100644 index 0000000..4332c0b --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/zero-code/README.rst @@ -0,0 +1,45 @@ +OpenTelemetry OpenAI Zero-Code Instrumentation Example +====================================================== + +This is an example of how to instrument OpenAI calls with zero code changes, +using `opentelemetry-instrument`. + +When `main.py `_ is run, it exports traces and logs to an OTLP +compatible endpoint. Traces include details such as the model used and the +duration of the chat request. Logs capture the chat request and the generated +response, providing a comprehensive view of the performance and behavior of +your OpenAI requests. + +Note: `.env <.env>`_ file configures additional environment variables: + +- ``OTEL_PYTHON_LOGGING_AUTO_INSTRUMENTATION_ENABLED=true`` configures OpenTelemetry SDK to export logs and events. +- ``OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=true`` configures OpenAI instrumentation to capture prompt and completion contents on events. +- ``OTEL_LOGS_EXPORTER=otlp`` to specify exporter type. + +Setup +----- + +Minimally, update the `.env <.env>`_ file with your ``OPENAI_API_KEY``. An +OTLP compatible endpoint should be listening for traces and logs on +http://localhost:4317. If not, update ``OTEL_EXPORTER_OTLP_ENDPOINT`` as well. + +Next, set up a virtual environment like this: + +:: + + python3 -m venv .venv + source .venv/bin/activate + pip install "python-dotenv[cli]" + pip install -r requirements.txt + +Run +--- + +Run the example like this: + +:: + + dotenv run -- opentelemetry-instrument python main.py + +You should see a poem generated by OpenAI while traces and logs export to your +configured observability tool. diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/zero-code/__init__.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/zero-code/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/zero-code/main.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/zero-code/main.py new file mode 100644 index 0000000..d41b1dd --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/zero-code/main.py @@ -0,0 +1,21 @@ +import os + +from openai import OpenAI + + +def main(): + client = OpenAI() + chat_completion = client.chat.completions.create( + model=os.getenv("CHAT_MODEL", "gpt-4o-mini"), + messages=[ + { + "role": "user", + "content": "Write a short poem on OpenTelemetry.", + }, + ], + ) + print(chat_completion.choices[0].message.content) + + +if __name__ == "__main__": + main() diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/zero-code/requirements.txt b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/zero-code/requirements.txt new file mode 100644 index 0000000..62a6a6e --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/examples/zero-code/requirements.txt @@ -0,0 +1,6 @@ +openai~=1.57.3 + +opentelemetry-sdk~=1.36.0 +opentelemetry-exporter-otlp-proto-grpc~=1.36.0 +opentelemetry-distro~=0.57b0 +opentelemetry-instrumentation-openai-v2~=2.1b0 diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/pyproject.toml b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/pyproject.toml new file mode 100644 index 0000000..6190dd2 --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/pyproject.toml @@ -0,0 +1,55 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "opentelemetry-instrumentation-openai-v2" +dynamic = ["version"] +description = "OpenTelemetry Official OpenAI instrumentation" +readme = "README.rst" +license = "Apache-2.0" +requires-python = ">=3.9" +authors = [ + { name = "OpenTelemetry Authors", email = "cncf-opentelemetry-contributors@lists.cncf.io" }, +] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", +] +dependencies = [ + "opentelemetry-api ~= 1.37", + "opentelemetry-instrumentation ~= 0.58b0", + "opentelemetry-semantic-conventions ~= 0.58b0" +] + +[project.optional-dependencies] +instruments = [ + "openai >= 1.26.0", +] + +[project.entry-points.opentelemetry_instrumentor] +openai = "opentelemetry.instrumentation.openai_v2:OpenAIInstrumentor" + +[project.urls] +Homepage = "https://github.com/open-telemetry/opentelemetry-python-contrib/tree/main/instrumentation-genai/opentelemetry-instrumentation-openai-v2" +Repository = "https://github.com/open-telemetry/opentelemetry-python-contrib" + +[tool.hatch.version] +path = "src/opentelemetry/instrumentation/openai_v2/version.py" + +[tool.hatch.build.targets.sdist] +include = [ + "/src", + "/tests", +] + +[tool.hatch.build.targets.wheel] +packages = ["src/opentelemetry"] diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/__init__.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/__init__.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/__init__.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/__init__.py new file mode 100644 index 0000000..9e4a248 --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/__init__.py @@ -0,0 +1,137 @@ +# 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. + +""" +OpenAI client instrumentation supporting `openai`, it can be enabled by +using ``OpenAIInstrumentor``. + +.. _openai: https://pypi.org/project/openai/ + +Usage +----- + +.. code:: python + + from openai import OpenAI + from opentelemetry.instrumentation.openai_v2 import OpenAIInstrumentor + + OpenAIInstrumentor().instrument() + + client = OpenAI() + response = client.chat.completions.create( + model="gpt-4o-mini", + messages=[ + {"role": "user", "content": "Write a short poem on open telemetry."}, + ], + ) + +API +--- +""" + +from typing import Collection + +from wrapt import wrap_function_wrapper + +from opentelemetry._logs import get_logger +from opentelemetry.instrumentation.instrumentor import BaseInstrumentor +from opentelemetry.instrumentation.openai_v2.package import _instruments +from opentelemetry.instrumentation.openai_v2.utils import is_content_enabled +from opentelemetry.instrumentation.utils import unwrap +from opentelemetry.metrics import get_meter +from opentelemetry.semconv.schemas import Schemas +from opentelemetry.trace import get_tracer + +from .instruments import Instruments +from .patch import ( + async_chat_completions_create, + async_embeddings_create, + chat_completions_create, + embeddings_create, +) + + +class OpenAIInstrumentor(BaseInstrumentor): + def __init__(self): + self._meter = None + + def instrumentation_dependencies(self) -> Collection[str]: + return _instruments + + def _instrument(self, **kwargs): + """Enable OpenAI instrumentation.""" + tracer_provider = kwargs.get("tracer_provider") + tracer = get_tracer( + __name__, + "", + tracer_provider, + schema_url=Schemas.V1_28_0.value, + ) + logger_provider = kwargs.get("logger_provider") + logger = get_logger( + __name__, + "", + schema_url=Schemas.V1_28_0.value, + logger_provider=logger_provider, + ) + meter_provider = kwargs.get("meter_provider") + self._meter = get_meter( + __name__, + "", + meter_provider, + schema_url=Schemas.V1_28_0.value, + ) + + instruments = Instruments(self._meter) + + wrap_function_wrapper( + module="openai.resources.chat.completions", + name="Completions.create", + wrapper=chat_completions_create( + tracer, logger, instruments, is_content_enabled() + ), + ) + + wrap_function_wrapper( + module="openai.resources.chat.completions", + name="AsyncCompletions.create", + wrapper=async_chat_completions_create( + tracer, logger, instruments, is_content_enabled() + ), + ) + + # Add instrumentation for the embeddings API + wrap_function_wrapper( + module="openai.resources.embeddings", + name="Embeddings.create", + wrapper=embeddings_create( + tracer, instruments, is_content_enabled() + ), + ) + + wrap_function_wrapper( + module="openai.resources.embeddings", + name="AsyncEmbeddings.create", + wrapper=async_embeddings_create( + tracer, instruments, is_content_enabled() + ), + ) + + def _uninstrument(self, **kwargs): + import openai # pylint: disable=import-outside-toplevel # noqa: PLC0415 + + unwrap(openai.resources.chat.completions.Completions, "create") + unwrap(openai.resources.chat.completions.AsyncCompletions, "create") + unwrap(openai.resources.embeddings.Embeddings, "create") + unwrap(openai.resources.embeddings.AsyncEmbeddings, "create") diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/instruments.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/instruments.py new file mode 100644 index 0000000..70c1005 --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/instruments.py @@ -0,0 +1,52 @@ +from opentelemetry.metrics import Histogram, Meter +from opentelemetry.semconv._incubating.metrics import gen_ai_metrics + +_GEN_AI_CLIENT_OPERATION_DURATION_BUCKETS = [ + 0.01, + 0.02, + 0.04, + 0.08, + 0.16, + 0.32, + 0.64, + 1.28, + 2.56, + 5.12, + 10.24, + 20.48, + 40.96, + 81.92, +] + +_GEN_AI_CLIENT_TOKEN_USAGE_BUCKETS = [ + 1, + 4, + 16, + 64, + 256, + 1024, + 4096, + 16384, + 65536, + 262144, + 1048576, + 4194304, + 16777216, + 67108864, +] + + +class Instruments: + def __init__(self, meter: Meter): + self.operation_duration_histogram: Histogram = meter.create_histogram( + name=gen_ai_metrics.GEN_AI_CLIENT_OPERATION_DURATION, + description="GenAI operation duration", + unit="s", + explicit_bucket_boundaries_advisory=_GEN_AI_CLIENT_OPERATION_DURATION_BUCKETS, + ) + self.token_usage_histogram: Histogram = meter.create_histogram( + name=gen_ai_metrics.GEN_AI_CLIENT_TOKEN_USAGE, + description="Measures number of input and output tokens used", + unit="{token}", + explicit_bucket_boundaries_advisory=_GEN_AI_CLIENT_TOKEN_USAGE_BUCKETS, + ) diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/package.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/package.py new file mode 100644 index 0000000..4973d36 --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/package.py @@ -0,0 +1,18 @@ +# 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. + + +_instruments = ("openai >= 1.26.0",) + +_supports_metrics = True diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/patch.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/patch.py new file mode 100644 index 0000000..b51a8aa --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/patch.py @@ -0,0 +1,688 @@ +# 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. + + +from timeit import default_timer +from typing import Any, Optional + +from openai import Stream + +from opentelemetry._logs import Logger, LogRecord +from opentelemetry.context import get_current +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAIAttributes, +) +from opentelemetry.semconv._incubating.attributes import ( + server_attributes as ServerAttributes, +) +from opentelemetry.trace import Span, SpanKind, Tracer +from opentelemetry.trace.propagation import set_span_in_context + +from .instruments import Instruments +from .utils import ( + choice_to_event, + get_llm_request_attributes, + handle_span_exception, + is_streaming, + message_to_event, + set_span_attribute, +) + + +def chat_completions_create( + tracer: Tracer, + logger: Logger, + instruments: Instruments, + capture_content: bool, +): + """Wrap the `create` method of the `ChatCompletion` class to trace it.""" + + def traced_method(wrapped, instance, args, kwargs): + span_attributes = {**get_llm_request_attributes(kwargs, instance)} + + span_name = f"{span_attributes[GenAIAttributes.GEN_AI_OPERATION_NAME]} {span_attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL]}" + with tracer.start_as_current_span( + name=span_name, + kind=SpanKind.CLIENT, + attributes=span_attributes, + end_on_exit=False, + ) as span: + for message in kwargs.get("messages", []): + logger.emit(message_to_event(message, capture_content)) + + start = default_timer() + result = None + error_type = None + try: + result = wrapped(*args, **kwargs) + if is_streaming(kwargs): + return StreamWrapper(result, span, logger, capture_content) + + if span.is_recording(): + _set_response_attributes( + span, result, logger, capture_content + ) + for choice in getattr(result, "choices", []): + logger.emit(choice_to_event(choice, capture_content)) + + span.end() + return result + + except Exception as error: + error_type = type(error).__qualname__ + handle_span_exception(span, error) + raise + finally: + duration = max((default_timer() - start), 0) + _record_metrics( + instruments, + duration, + result, + span_attributes, + error_type, + GenAIAttributes.GenAiOperationNameValues.CHAT.value, + ) + + return traced_method + + +def async_chat_completions_create( + tracer: Tracer, + logger: Logger, + instruments: Instruments, + capture_content: bool, +): + """Wrap the `create` method of the `AsyncChatCompletion` class to trace it.""" + + async def traced_method(wrapped, instance, args, kwargs): + span_attributes = {**get_llm_request_attributes(kwargs, instance)} + + span_name = f"{span_attributes[GenAIAttributes.GEN_AI_OPERATION_NAME]} {span_attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL]}" + with tracer.start_as_current_span( + name=span_name, + kind=SpanKind.CLIENT, + attributes=span_attributes, + end_on_exit=False, + ) as span: + for message in kwargs.get("messages", []): + logger.emit(message_to_event(message, capture_content)) + + start = default_timer() + result = None + error_type = None + try: + result = await wrapped(*args, **kwargs) + if is_streaming(kwargs): + return StreamWrapper(result, span, logger, capture_content) + + if span.is_recording(): + _set_response_attributes( + span, result, logger, capture_content + ) + for choice in getattr(result, "choices", []): + logger.emit(choice_to_event(choice, capture_content)) + + span.end() + return result + + except Exception as error: + error_type = type(error).__qualname__ + handle_span_exception(span, error) + raise + finally: + duration = max((default_timer() - start), 0) + _record_metrics( + instruments, + duration, + result, + span_attributes, + error_type, + GenAIAttributes.GenAiOperationNameValues.CHAT.value, + ) + + return traced_method + + +def embeddings_create( + tracer: Tracer, + instruments: Instruments, + capture_content: bool, +): + """Wrap the `create` method of the `Embeddings` class to trace it.""" + + def traced_method(wrapped, instance, args, kwargs): + span_attributes = get_llm_request_attributes( + kwargs, + instance, + GenAIAttributes.GenAiOperationNameValues.EMBEDDINGS.value, + ) + span_name = _get_embeddings_span_name(span_attributes) + input_text = kwargs.get("input", "") + + with tracer.start_as_current_span( + name=span_name, + kind=SpanKind.CLIENT, + attributes=span_attributes, + end_on_exit=True, + ) as span: + start = default_timer() + result = None + error_type = None + + try: + result = wrapped(*args, **kwargs) + + if span.is_recording(): + _set_embeddings_response_attributes( + span, result, capture_content, input_text + ) + + return result + + except Exception as error: + error_type = type(error).__qualname__ + handle_span_exception(span, error) + raise + + finally: + duration = max((default_timer() - start), 0) + _record_metrics( + instruments, + duration, + result, + span_attributes, + error_type, + GenAIAttributes.GenAiOperationNameValues.EMBEDDINGS.value, + ) + + return traced_method + + +def async_embeddings_create( + tracer: Tracer, + instruments: Instruments, + capture_content: bool, +): + """Wrap the `create` method of the `AsyncEmbeddings` class to trace it.""" + + async def traced_method(wrapped, instance, args, kwargs): + span_attributes = get_llm_request_attributes( + kwargs, + instance, + GenAIAttributes.GenAiOperationNameValues.EMBEDDINGS.value, + ) + span_name = _get_embeddings_span_name(span_attributes) + input_text = kwargs.get("input", "") + + with tracer.start_as_current_span( + name=span_name, + kind=SpanKind.CLIENT, + attributes=span_attributes, + end_on_exit=True, + ) as span: + start = default_timer() + result = None + error_type = None + + try: + result = await wrapped(*args, **kwargs) + + if span.is_recording(): + _set_embeddings_response_attributes( + span, result, capture_content, input_text + ) + + return result + + except Exception as error: + error_type = type(error).__qualname__ + handle_span_exception(span, error) + raise + + finally: + duration = max((default_timer() - start), 0) + _record_metrics( + instruments, + duration, + result, + span_attributes, + error_type, + GenAIAttributes.GenAiOperationNameValues.EMBEDDINGS.value, + ) + + return traced_method + + +def _get_embeddings_span_name(span_attributes): + """Get span name for embeddings operations.""" + return f"{span_attributes[GenAIAttributes.GEN_AI_OPERATION_NAME]} {span_attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL]}" + + +def _record_metrics( + instruments: Instruments, + duration: float, + result, + request_attributes: dict, + error_type: Optional[str], + operation_name: str, +): + common_attributes = { + GenAIAttributes.GEN_AI_OPERATION_NAME: operation_name, + GenAIAttributes.GEN_AI_SYSTEM: GenAIAttributes.GenAiSystemValues.OPENAI.value, + GenAIAttributes.GEN_AI_REQUEST_MODEL: request_attributes[ + GenAIAttributes.GEN_AI_REQUEST_MODEL + ], + } + + if "gen_ai.embeddings.dimension.count" in request_attributes: + common_attributes["gen_ai.embeddings.dimension.count"] = ( + request_attributes["gen_ai.embeddings.dimension.count"] + ) + + if error_type: + common_attributes["error.type"] = error_type + + if result and getattr(result, "model", None): + common_attributes[GenAIAttributes.GEN_AI_RESPONSE_MODEL] = result.model + + if result and getattr(result, "service_tier", None): + common_attributes[ + GenAIAttributes.GEN_AI_OPENAI_RESPONSE_SERVICE_TIER + ] = result.service_tier + + if result and getattr(result, "system_fingerprint", None): + common_attributes["gen_ai.openai.response.system_fingerprint"] = ( + result.system_fingerprint + ) + + if ServerAttributes.SERVER_ADDRESS in request_attributes: + common_attributes[ServerAttributes.SERVER_ADDRESS] = ( + request_attributes[ServerAttributes.SERVER_ADDRESS] + ) + + if ServerAttributes.SERVER_PORT in request_attributes: + common_attributes[ServerAttributes.SERVER_PORT] = request_attributes[ + ServerAttributes.SERVER_PORT + ] + + instruments.operation_duration_histogram.record( + duration, + attributes=common_attributes, + ) + + if result and getattr(result, "usage", None): + # Always record input tokens + input_attributes = { + **common_attributes, + GenAIAttributes.GEN_AI_TOKEN_TYPE: GenAIAttributes.GenAiTokenTypeValues.INPUT.value, + } + instruments.token_usage_histogram.record( + result.usage.prompt_tokens, + attributes=input_attributes, + ) + + # For embeddings, don't record output tokens as all tokens are input tokens + if ( + operation_name + != GenAIAttributes.GenAiOperationNameValues.EMBEDDINGS.value + ): + output_attributes = { + **common_attributes, + GenAIAttributes.GEN_AI_TOKEN_TYPE: GenAIAttributes.GenAiTokenTypeValues.COMPLETION.value, + } + instruments.token_usage_histogram.record( + result.usage.completion_tokens, attributes=output_attributes + ) + + +def _set_response_attributes( + span, result, logger: Logger, capture_content: bool +): + set_span_attribute( + span, GenAIAttributes.GEN_AI_RESPONSE_MODEL, result.model + ) + + if getattr(result, "choices", None): + finish_reasons = [] + for choice in result.choices: + finish_reasons.append(choice.finish_reason or "error") + + set_span_attribute( + span, + GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS, + finish_reasons, + ) + + if getattr(result, "id", None): + set_span_attribute(span, GenAIAttributes.GEN_AI_RESPONSE_ID, result.id) + + if getattr(result, "service_tier", None): + set_span_attribute( + span, + GenAIAttributes.GEN_AI_OPENAI_RESPONSE_SERVICE_TIER, + result.service_tier, + ) + + # Get the usage + if getattr(result, "usage", None): + set_span_attribute( + span, + GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS, + result.usage.prompt_tokens, + ) + set_span_attribute( + span, + GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS, + result.usage.completion_tokens, + ) + + +def _set_embeddings_response_attributes( + span: Span, + result: Any, + capture_content: bool, + input_text: str, +): + set_span_attribute( + span, GenAIAttributes.GEN_AI_RESPONSE_MODEL, result.model + ) + + # Set embeddings dimensions if we can determine it from the response + if getattr(result, "data", None) and len(result.data) > 0: + first_embedding = result.data[0] + if getattr(first_embedding, "embedding", None): + set_span_attribute( + span, + "gen_ai.embeddings.dimension.count", + len(first_embedding.embedding), + ) + + # Get the usage + if getattr(result, "usage", None): + set_span_attribute( + span, + GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS, + result.usage.prompt_tokens, + ) + # Don't set output tokens for embeddings as all tokens are input tokens + + +class ToolCallBuffer: + def __init__(self, index, tool_call_id, function_name): + self.index = index + self.function_name = function_name + self.tool_call_id = tool_call_id + self.arguments = [] + + def append_arguments(self, arguments): + self.arguments.append(arguments) + + +class ChoiceBuffer: + def __init__(self, index): + self.index = index + self.finish_reason = None + self.text_content = [] + self.tool_calls_buffers = [] + + def append_text_content(self, content): + self.text_content.append(content) + + def append_tool_call(self, tool_call): + idx = tool_call.index + # make sure we have enough tool call buffers + for _ in range(len(self.tool_calls_buffers), idx + 1): + self.tool_calls_buffers.append(None) + + if not self.tool_calls_buffers[idx]: + self.tool_calls_buffers[idx] = ToolCallBuffer( + idx, tool_call.id, tool_call.function.name + ) + self.tool_calls_buffers[idx].append_arguments( + tool_call.function.arguments + ) + + +class StreamWrapper: + span: Span + response_id: Optional[str] = None + response_model: Optional[str] = None + service_tier: Optional[str] = None + finish_reasons: list = [] + prompt_tokens: Optional[int] = 0 + completion_tokens: Optional[int] = 0 + + def __init__( + self, + stream: Stream, + span: Span, + logger: Logger, + capture_content: bool, + ): + self.stream = stream + self.span = span + self.choice_buffers = [] + self._span_started = False + self.capture_content = capture_content + + self.logger = logger + self.setup() + + def setup(self): + if not self._span_started: + self._span_started = True + + def cleanup(self): + if self._span_started: + if self.span.is_recording(): + if self.response_model: + set_span_attribute( + self.span, + GenAIAttributes.GEN_AI_RESPONSE_MODEL, + self.response_model, + ) + + if self.response_id: + set_span_attribute( + self.span, + GenAIAttributes.GEN_AI_RESPONSE_ID, + self.response_id, + ) + + set_span_attribute( + self.span, + GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS, + self.prompt_tokens, + ) + set_span_attribute( + self.span, + GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS, + self.completion_tokens, + ) + + set_span_attribute( + self.span, + GenAIAttributes.GEN_AI_OPENAI_RESPONSE_SERVICE_TIER, + self.service_tier, + ) + + set_span_attribute( + self.span, + GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS, + self.finish_reasons, + ) + + for idx, choice in enumerate(self.choice_buffers): + message = {"role": "assistant"} + if self.capture_content and choice.text_content: + message["content"] = "".join(choice.text_content) + if choice.tool_calls_buffers: + tool_calls = [] + for tool_call in choice.tool_calls_buffers: + function = {"name": tool_call.function_name} + if self.capture_content: + function["arguments"] = "".join( + tool_call.arguments + ) + tool_call_dict = { + "id": tool_call.tool_call_id, + "type": "function", + "function": function, + } + tool_calls.append(tool_call_dict) + message["tool_calls"] = tool_calls + + body = { + "index": idx, + "finish_reason": choice.finish_reason or "error", + "message": message, + } + + event_attributes = { + GenAIAttributes.GEN_AI_SYSTEM: GenAIAttributes.GenAiSystemValues.OPENAI.value + } + context = set_span_in_context(self.span, get_current()) + self.logger.emit( + LogRecord( + event_name="gen_ai.choice", + attributes=event_attributes, + body=body, + context=context, + ) + ) + + self.span.end() + self._span_started = False + + def __enter__(self): + self.setup() + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + try: + if exc_type is not None: + handle_span_exception(self.span, exc_val) + finally: + self.cleanup() + return False # Propagate the exception + + async def __aenter__(self): + self.setup() + return self + + async def __aexit__(self, exc_type, exc_val, exc_tb): + try: + if exc_type is not None: + handle_span_exception(self.span, exc_val) + finally: + self.cleanup() + return False # Propagate the exception + + def close(self): + self.stream.close() + self.cleanup() + + def __iter__(self): + return self + + def __aiter__(self): + return self + + def __next__(self): + try: + chunk = next(self.stream) + self.process_chunk(chunk) + return chunk + except StopIteration: + self.cleanup() + raise + except Exception as error: + handle_span_exception(self.span, error) + self.cleanup() + raise + + async def __anext__(self): + try: + chunk = await self.stream.__anext__() + self.process_chunk(chunk) + return chunk + except StopAsyncIteration: + self.cleanup() + raise + except Exception as error: + handle_span_exception(self.span, error) + self.cleanup() + raise + + def set_response_model(self, chunk): + if self.response_model: + return + + if getattr(chunk, "model", None): + self.response_model = chunk.model + + def set_response_id(self, chunk): + if self.response_id: + return + + if getattr(chunk, "id", None): + self.response_id = chunk.id + + def set_response_service_tier(self, chunk): + if self.service_tier: + return + + if getattr(chunk, "service_tier", None): + self.service_tier = chunk.service_tier + + def build_streaming_response(self, chunk): + if getattr(chunk, "choices", None) is None: + return + + choices = chunk.choices + for choice in choices: + if not choice.delta: + continue + + # make sure we have enough choice buffers + for idx in range(len(self.choice_buffers), choice.index + 1): + self.choice_buffers.append(ChoiceBuffer(idx)) + + if choice.finish_reason: + 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 + ) + + 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): + self.completion_tokens = chunk.usage.completion_tokens + self.prompt_tokens = chunk.usage.prompt_tokens + + 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) + self.set_usage(chunk) diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/utils.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/utils.py new file mode 100644 index 0000000..2b723fb --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/utils.py @@ -0,0 +1,276 @@ +# 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. + +from __future__ import annotations + +from os import environ +from typing import Mapping +from urllib.parse import urlparse + +from httpx import URL +from openai import NotGiven + +from opentelemetry._logs import LogRecord +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAIAttributes, +) +from opentelemetry.semconv._incubating.attributes import ( + server_attributes as ServerAttributes, +) +from opentelemetry.semconv.attributes import ( + error_attributes as ErrorAttributes, +) +from opentelemetry.trace.status import Status, StatusCode + +OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT = ( + "OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT" +) + + +def is_content_enabled() -> bool: + capture_content = environ.get( + OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT, "false" + ) + + return capture_content.lower() == "true" + + +def extract_tool_calls(item, capture_content): + tool_calls = get_property_value(item, "tool_calls") + if tool_calls is None: + return None + + calls = [] + for tool_call in tool_calls: + 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 + + calls.append(tool_call_dict) + return calls + + +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 LogRecord( + 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 LogRecord( + event_name="gen_ai.choice", + attributes=attributes, + body=body, + ) + + +def set_span_attributes(span, attributes: dict): + for field, value in attributes.model_dump(by_alias=True).items(): + set_span_attribute(span, field, value) + + +def set_span_attribute(span, name, value): + if non_numerical_value_is_set(value) is False: + return + + span.set_attribute(name, value) + + +def is_streaming(kwargs): + return non_numerical_value_is_set(kwargs.get("stream")) + + +def non_numerical_value_is_set(value: bool | str | NotGiven | None): + return bool(value) and value_is_set(value) + + +def value_is_set(value): + return value is not None and not isinstance(value, NotGiven) + + +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"), + } + + # Add chat-specific attributes only for chat operations + if operation_name == GenAIAttributes.GenAiOperationNameValues.CHAT.value: + attributes.update( + { + GenAIAttributes.GEN_AI_REQUEST_TEMPERATURE: kwargs.get( + "temperature" + ), + GenAIAttributes.GEN_AI_REQUEST_TOP_P: kwargs.get("p") + or kwargs.get("top_p"), + GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS: kwargs.get( + "max_tokens" + ), + GenAIAttributes.GEN_AI_REQUEST_PRESENCE_PENALTY: kwargs.get( + "presence_penalty" + ), + GenAIAttributes.GEN_AI_REQUEST_FREQUENCY_PENALTY: kwargs.get( + "frequency_penalty" + ), + GenAIAttributes.GEN_AI_OPENAI_REQUEST_SEED: kwargs.get("seed"), + } + ) + + if (response_format := kwargs.get("response_format")) is not None: + # response_format may be string or object with a string in the `type` key + if isinstance(response_format, Mapping): + if ( + response_format_type := response_format.get("type") + ) is not None: + attributes[ + GenAIAttributes.GEN_AI_OPENAI_REQUEST_RESPONSE_FORMAT + ] = response_format_type + else: + attributes[ + GenAIAttributes.GEN_AI_OPENAI_REQUEST_RESPONSE_FORMAT + ] = response_format + + # service_tier can be passed directly or in extra_body (in SDK 1.26.0 it's via extra_body) + service_tier = kwargs.get("service_tier") + if service_tier is None: + extra_body = kwargs.get("extra_body") + if isinstance(extra_body, Mapping): + service_tier = extra_body.get("service_tier") + attributes[GenAIAttributes.GEN_AI_OPENAI_REQUEST_SERVICE_TIER] = ( + service_tier if service_tier != "auto" else None + ) + + # Add embeddings-specific attributes + elif ( + operation_name + == GenAIAttributes.GenAiOperationNameValues.EMBEDDINGS.value + ): + # Add embedding dimensions if specified + if (dimensions := kwargs.get("dimensions")) is not None: + attributes["gen_ai.embeddings.dimension.count"] = dimensions + + # Add encoding format if specified + if "encoding_format" in kwargs: + attributes["gen_ai.request.encoding_formats"] = [ + kwargs["encoding_format"] + ] + + set_server_address_and_port(client_instance, attributes) + + # filter out values not set + return {k: v for k, v in attributes.items() if value_is_set(v)} + + +def handle_span_exception(span, error): + span.set_status(Status(StatusCode.ERROR, str(error))) + if span.is_recording(): + span.set_attribute( + ErrorAttributes.ERROR_TYPE, type(error).__qualname__ + ) + span.end() diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/version.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/version.py new file mode 100644 index 0000000..1cfac0a --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/version.py @@ -0,0 +1,15 @@ +# 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. + +__version__ = "2.3b0.dev" diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/README.md b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/README.md new file mode 100644 index 0000000..5ccd714 --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/README.md @@ -0,0 +1,10 @@ +## Recording calls + +If you need to record calls you need to export the `OPENAI_API_KEY` as environment variable. +Since tox blocks environment variables by default you need to override its configuration to let them pass: + +``` +export TOX_OVERRIDE=testenv.pass_env=OPENAI_API_KEY +``` + +We are not adding it to tox.ini because of security concerns. diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/__init__.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/cassettes/test_async_chat_completion_404.yaml b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/cassettes/test_async_chat_completion_404.yaml new file mode 100644 index 0000000..e055e68 --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/cassettes/test_async_chat_completion_404.yaml @@ -0,0 +1,89 @@ +interactions: +- request: + body: |- + { + "messages": [ + { + "role": "user", + "content": "Say this is a test" + } + ], + "model": "this-model-does-not-exist" + } + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate + authorization: + - Bearer test_openai_api_key + connection: + - keep-alive + content-length: + - '103' + content-type: + - application/json + host: + - api.openai.com + user-agent: + - AsyncOpenAI/Python 1.26.0 + x-stainless-arch: + - arm64 + x-stainless-async: + - async:asyncio + x-stainless-lang: + - python + x-stainless-os: + - MacOS + x-stainless-package-version: + - 1.26.0 + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.12.5 + method: POST + uri: https://api.openai.com/v1/chat/completions + response: + body: + string: |- + { + "error": { + "message": "The model `this-model-does-not-exist` does not exist or you do not have access to it.", + "type": "invalid_request_error", + "param": null, + "code": "model_not_found" + } + } + headers: + CF-Cache-Status: + - DYNAMIC + CF-RAY: + - 8e1a80827a861852-MRS + Connection: + - keep-alive + Content-Type: + - application/json; charset=utf-8 + Date: + - Wed, 13 Nov 2024 00:04:01 GMT + Server: + - cloudflare + Set-Cookie: test_set_cookie + Transfer-Encoding: + - chunked + X-Content-Type-Options: + - nosniff + alt-svc: + - h3=":443"; ma=86400 + content-length: + - '231' + openai-organization: test_openai_org_id + strict-transport-security: + - max-age=31536000; includeSubDomains; preload + vary: + - Origin + x-request-id: + - req_5cf06a7fabd45ebe21ee38c14c5b2f76 + status: + code: 404 + message: Not Found +version: 1 diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/cassettes/test_async_chat_completion_extra_params.yaml b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/cassettes/test_async_chat_completion_extra_params.yaml new file mode 100644 index 0000000..37c18b0 --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/cassettes/test_async_chat_completion_extra_params.yaml @@ -0,0 +1,140 @@ +interactions: +- request: + body: |- + { + "messages": [ + { + "role": "user", + "content": "Say this is a test" + } + ], + "model": "gpt-4o-mini", + "max_tokens": 50, + "response_format": { + "type": "text" + }, + "seed": 42, + "stream": false, + "temperature": 0.5, + "service_tier": "default" + } + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate + authorization: + - Bearer test_openai_api_key + connection: + - keep-alive + content-length: + - '220' + content-type: + - application/json + host: + - api.openai.com + user-agent: + - AsyncOpenAI/Python 1.26.0 + x-stainless-arch: + - arm64 + x-stainless-async: + - async:asyncio + x-stainless-lang: + - python + x-stainless-os: + - MacOS + x-stainless-package-version: + - 1.26.0 + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.12.7 + method: POST + uri: https://api.openai.com/v1/chat/completions + response: + body: + string: |- + { + "id": "chatcmpl-AbMH3rR6OBMN9hG5w0TRrezuiHLMr", + "object": "chat.completion", + "created": 1733467121, + "model": "gpt-4o-mini-2024-07-18", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "This is a test. 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+ } + ], + "model": "text-embedding-3-small", + "usage": { + "prompt_tokens": 9, + "total_tokens": 9 + } + } + headers: + CF-RAY: + - 99ac1f320cac4c6f-MXP + Connection: + - keep-alive + Content-Type: + - application/json + Date: + - Fri, 07 Nov 2025 10:24:37 GMT + Server: + - cloudflare + Set-Cookie: test_set_cookie + Transfer-Encoding: + - chunked + X-Content-Type-Options: + - nosniff + access-control-allow-origin: + - '*' + access-control-expose-headers: + - X-Request-ID + alt-svc: + - h3=":443"; ma=86400 + cf-cache-status: + - DYNAMIC + content-length: + - '8414' + openai-model: + - text-embedding-3-small + openai-organization: test_openai_org_id + openai-processing-ms: + - '58' + openai-project: + - proj_Pf1eM5R55Z35wBy4rt8PxAGq + openai-version: + - '2020-10-01' + strict-transport-security: + - max-age=31536000; includeSubDomains; preload + via: + - envoy-router-7cd555d77c-nhr8p + x-envoy-upstream-service-time: + - '867' + x-openai-proxy-wasm: + - v0.1 + x-ratelimit-limit-requests: + - '10000' + x-ratelimit-limit-tokens: + - '5000000' + x-ratelimit-remaining-requests: + - '9999' + x-ratelimit-remaining-tokens: + - '4999988' + x-ratelimit-reset-requests: + - 6ms + x-ratelimit-reset-tokens: + - 0s + x-request-id: + - req_2d5eb18c0eed49f1b12359871cb8d17d + status: + code: 200 + message: OK +version: 1 diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/conftest.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/conftest.py new file mode 100644 index 0000000..af626d7 --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/conftest.py @@ -0,0 +1,246 @@ +"""Unit tests configuration module.""" + +import json +import os + +import pytest +import yaml +from openai import AsyncOpenAI, OpenAI + +from opentelemetry.instrumentation.openai_v2 import OpenAIInstrumentor +from opentelemetry.instrumentation.openai_v2.utils import ( + OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT, +) +from opentelemetry.sdk._logs import LoggerProvider + +# Backward compatibility for InMemoryLogExporter -> InMemoryLogRecordExporter rename +try: + from opentelemetry.sdk._logs.export import ( # pylint: disable=no-name-in-module + InMemoryLogRecordExporter, + SimpleLogRecordProcessor, + ) +except ImportError: + # Fallback to old name for compatibility with older SDK versions + from opentelemetry.sdk._logs.export import ( + InMemoryLogExporter as InMemoryLogRecordExporter, + ) + from opentelemetry.sdk._logs.export import ( + SimpleLogRecordProcessor, + ) +from opentelemetry.sdk.metrics import ( + MeterProvider, +) +from opentelemetry.sdk.metrics.export import ( + InMemoryMetricReader, +) +from opentelemetry.sdk.trace import TracerProvider +from opentelemetry.sdk.trace.export import SimpleSpanProcessor +from opentelemetry.sdk.trace.export.in_memory_span_exporter import ( + InMemorySpanExporter, +) +from opentelemetry.sdk.trace.sampling import ALWAYS_OFF + + +@pytest.fixture(scope="function", name="span_exporter") +def fixture_span_exporter(): + exporter = InMemorySpanExporter() + yield exporter + + +@pytest.fixture(scope="function", name="log_exporter") +def fixture_log_exporter(): + exporter = InMemoryLogRecordExporter() + yield exporter + + +@pytest.fixture(scope="function", name="metric_reader") +def fixture_metric_reader(): + exporter = InMemoryMetricReader() + yield exporter + + +@pytest.fixture(scope="function", name="tracer_provider") +def fixture_tracer_provider(span_exporter): + provider = TracerProvider() + provider.add_span_processor(SimpleSpanProcessor(span_exporter)) + return provider + + +@pytest.fixture(scope="function", name="logger_provider") +def fixture_logger_provider(log_exporter): + provider = LoggerProvider() + provider.add_log_record_processor(SimpleLogRecordProcessor(log_exporter)) + return provider + + +@pytest.fixture(scope="function", name="meter_provider") +def fixture_meter_provider(metric_reader): + meter_provider = MeterProvider( + metric_readers=[metric_reader], + ) + + return meter_provider + + +@pytest.fixture(autouse=True) +def environment(): + if not os.getenv("OPENAI_API_KEY"): + os.environ["OPENAI_API_KEY"] = "test_openai_api_key" + + +@pytest.fixture +def openai_client(): + return OpenAI() + + +@pytest.fixture +def async_openai_client(): + return AsyncOpenAI() + + +@pytest.fixture(scope="module") +def vcr_config(): + return { + "filter_headers": [ + ("cookie", "test_cookie"), + ("authorization", "Bearer test_openai_api_key"), + ("openai-organization", "test_openai_org_id"), + ("openai-project", "test_openai_project_id"), + ], + "decode_compressed_response": True, + "before_record_response": scrub_response_headers, + } + + +@pytest.fixture(scope="function") +def instrument_no_content(tracer_provider, logger_provider, meter_provider): + os.environ.update( + {OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT: "False"} + ) + + instrumentor = OpenAIInstrumentor() + instrumentor.instrument( + tracer_provider=tracer_provider, + logger_provider=logger_provider, + meter_provider=meter_provider, + ) + + yield instrumentor + os.environ.pop(OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT, None) + instrumentor.uninstrument() + + +@pytest.fixture(scope="function") +def instrument_with_content(tracer_provider, logger_provider, meter_provider): + os.environ.update( + {OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT: "True"} + ) + instrumentor = OpenAIInstrumentor() + instrumentor.instrument( + tracer_provider=tracer_provider, + logger_provider=logger_provider, + meter_provider=meter_provider, + ) + + yield instrumentor + os.environ.pop(OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT, None) + instrumentor.uninstrument() + + +@pytest.fixture(scope="function") +def instrument_with_content_unsampled( + span_exporter, logger_provider, meter_provider +): + os.environ.update( + {OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT: "True"} + ) + + tracer_provider = TracerProvider(sampler=ALWAYS_OFF) + tracer_provider.add_span_processor(SimpleSpanProcessor(span_exporter)) + + instrumentor = OpenAIInstrumentor() + instrumentor.instrument( + tracer_provider=tracer_provider, + logger_provider=logger_provider, + meter_provider=meter_provider, + ) + + yield instrumentor + os.environ.pop(OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT, None) + instrumentor.uninstrument() + + +class LiteralBlockScalar(str): + """Formats the string as a literal block scalar, preserving whitespace and + without interpreting escape characters""" + + +def literal_block_scalar_presenter(dumper, data): + """Represents a scalar string as a literal block, via '|' syntax""" + return dumper.represent_scalar("tag:yaml.org,2002:str", data, style="|") + + +yaml.add_representer(LiteralBlockScalar, literal_block_scalar_presenter) + + +def process_string_value(string_value): + """Pretty-prints JSON or returns long strings as a LiteralBlockScalar""" + try: + json_data = json.loads(string_value) + return LiteralBlockScalar(json.dumps(json_data, indent=2)) + except (ValueError, TypeError): + if len(string_value) > 80: + return LiteralBlockScalar(string_value) + return string_value + + +def convert_body_to_literal(data): + """Searches the data for body strings, attempting to pretty-print JSON""" + if isinstance(data, dict): + for key, value in data.items(): + # Handle response body case (e.g., response.body.string) + if key == "body" and isinstance(value, dict) and "string" in value: + value["string"] = process_string_value(value["string"]) + + # Handle request body case (e.g., request.body) + elif key == "body" and isinstance(value, str): + data[key] = process_string_value(value) + + else: + convert_body_to_literal(value) + + elif isinstance(data, list): + for idx, choice in enumerate(data): + data[idx] = convert_body_to_literal(choice) + + return data + + +class PrettyPrintJSONBody: + """This makes request and response body recordings more readable.""" + + @staticmethod + def serialize(cassette_dict): + cassette_dict = convert_body_to_literal(cassette_dict) + return yaml.dump( + cassette_dict, default_flow_style=False, allow_unicode=True + ) + + @staticmethod + def deserialize(cassette_string): + return yaml.load(cassette_string, Loader=yaml.Loader) + + +@pytest.fixture(scope="module", autouse=True) +def fixture_vcr(vcr): + vcr.register_serializer("yaml", PrettyPrintJSONBody) + return vcr + + +def scrub_response_headers(response): + """ + This scrubs sensitive response headers. Note they are case-sensitive! + """ + response["headers"]["openai-organization"] = "test_openai_org_id" + response["headers"]["Set-Cookie"] = "test_set_cookie" + return response diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/requirements.latest.txt b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/requirements.latest.txt new file mode 100644 index 0000000..2e29be5 --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/requirements.latest.txt @@ -0,0 +1,54 @@ +# 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. + + +# ******************************** +# WARNING: NOT HERMETIC !!!!!!!!!! +# ******************************** +# +# This "requirements.txt" is installed in conjunction +# with multiple other dependencies in the top-level "tox.ini" +# file. In particular, please see: +# +# openai-latest: {[testenv]test_deps} +# openai-latest: -r {toxinidir}/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/requirements.latest.txt +# +# This provides additional dependencies, namely: +# +# opentelemetry-api +# opentelemetry-sdk +# opentelemetry-semantic-conventions +# +# ... with a "dev" version based on the latest distribution. + + +# This variant of the requirements aims to test the system using +# the newest supported version of external dependencies. + +openai==1.109.1 +pydantic==2.8.2 +httpx==0.27.2 +# older jiter is required for PyPy < 3.11 +jiter==0.11.1 +Deprecated==1.2.14 +importlib-metadata==6.11.0 +packaging==24.0 +pytest==7.4.4 +pytest-vcr==1.0.2 +pytest-asyncio==0.21.0 +wrapt==1.16.0 +# test with the latest version of opentelemetry-api, sdk, and semantic conventions + +-e opentelemetry-instrumentation +-e instrumentation-genai/opentelemetry-instrumentation-openai-v2 diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/requirements.oldest.txt b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/requirements.oldest.txt new file mode 100644 index 0000000..7026f44 --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/requirements.oldest.txt @@ -0,0 +1,33 @@ +# 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. + +# This variant of the requirements aims to test the system using +# the oldest supported version of external dependencies. + +openai[datalib]==1.26.0 +pydantic==2.8.2 +httpx==0.27.2 +Deprecated==1.2.14 +importlib-metadata==6.11.0 +packaging==24.0 +pytest==7.4.4 +pytest-vcr==1.0.2 +pytest-asyncio==0.21.0 +wrapt==1.16.0 +opentelemetry-exporter-otlp-proto-http~=1.30 +opentelemetry-api==1.37 # when updating, also update in pyproject.toml +opentelemetry-sdk==1.37 # when updating, also update in pyproject.toml +opentelemetry-semantic-conventions==0.58b0 # when updating, also update in pyproject.toml + +-e instrumentation-genai/opentelemetry-instrumentation-openai-v2 diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/test_async_chat_completions.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/test_async_chat_completions.py new file mode 100644 index 0000000..59594c0 --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/test_async_chat_completions.py @@ -0,0 +1,878 @@ +# 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 + + +import pytest +from openai import APIConnectionError, AsyncOpenAI, NotFoundError + +from opentelemetry.semconv._incubating.attributes import ( + error_attributes as ErrorAttributes, +) +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAIAttributes, +) +from opentelemetry.semconv._incubating.attributes import ( + server_attributes as ServerAttributes, +) + +from .test_utils import ( + assert_all_attributes, + assert_log_parent, + remove_none_values, +) + + +@pytest.mark.vcr() +@pytest.mark.asyncio() +async def test_async_chat_completion_with_content( + span_exporter, log_exporter, async_openai_client, instrument_with_content +): + llm_model_value = "gpt-4o-mini" + messages_value = [{"role": "user", "content": "Say this is a test"}] + + response = await async_openai_client.chat.completions.create( + messages=messages_value, model=llm_model_value, stream=False + ) + + spans = span_exporter.get_finished_spans() + assert_all_attributes( + spans[0], + llm_model_value, + response.id, + response.model, + response.usage.prompt_tokens, + response.usage.completion_tokens, + ) + + logs = log_exporter.get_finished_logs() + assert len(logs) == 2 + + user_message = {"content": messages_value[0]["content"]} + assert_message_in_logs( + logs[0], "gen_ai.user.message", user_message, spans[0] + ) + + choice_event = { + "index": 0, + "finish_reason": "stop", + "message": { + "role": "assistant", + "content": response.choices[0].message.content, + }, + } + assert_message_in_logs(logs[1], "gen_ai.choice", choice_event, spans[0]) + + +@pytest.mark.vcr() +@pytest.mark.asyncio() +async def test_async_chat_completion_no_content( + span_exporter, log_exporter, async_openai_client, instrument_no_content +): + llm_model_value = "gpt-4o-mini" + messages_value = [{"role": "user", "content": "Say this is a test"}] + + response = await async_openai_client.chat.completions.create( + messages=messages_value, model=llm_model_value, stream=False + ) + + spans = span_exporter.get_finished_spans() + assert_all_attributes( + spans[0], + llm_model_value, + response.id, + response.model, + response.usage.prompt_tokens, + response.usage.completion_tokens, + ) + + logs = log_exporter.get_finished_logs() + assert len(logs) == 2 + + assert_message_in_logs(logs[0], "gen_ai.user.message", None, spans[0]) + + choice_event = { + "index": 0, + "finish_reason": "stop", + "message": {"role": "assistant"}, + } + assert_message_in_logs(logs[1], "gen_ai.choice", choice_event, spans[0]) + + +@pytest.mark.asyncio() +async def test_async_chat_completion_bad_endpoint( + span_exporter, instrument_no_content +): + llm_model_value = "gpt-4o-mini" + messages_value = [{"role": "user", "content": "Say this is a test"}] + + client = AsyncOpenAI(base_url="http://localhost:4242") + + with pytest.raises(APIConnectionError): + await client.chat.completions.create( + messages=messages_value, + model=llm_model_value, + timeout=0.1, + ) + + spans = span_exporter.get_finished_spans() + assert_all_attributes( + spans[0], llm_model_value, server_address="localhost" + ) + assert 4242 == spans[0].attributes[ServerAttributes.SERVER_PORT] + assert ( + "APIConnectionError" == spans[0].attributes[ErrorAttributes.ERROR_TYPE] + ) + + +@pytest.mark.vcr() +@pytest.mark.asyncio() +async def test_async_chat_completion_404( + span_exporter, async_openai_client, instrument_no_content +): + llm_model_value = "this-model-does-not-exist" + messages_value = [{"role": "user", "content": "Say this is a test"}] + + with pytest.raises(NotFoundError): + await async_openai_client.chat.completions.create( + messages=messages_value, + model=llm_model_value, + ) + + spans = span_exporter.get_finished_spans() + + assert_all_attributes(spans[0], llm_model_value) + assert "NotFoundError" == spans[0].attributes[ErrorAttributes.ERROR_TYPE] + + +@pytest.mark.vcr() +@pytest.mark.asyncio() +async def test_async_chat_completion_extra_params( + span_exporter, async_openai_client, instrument_no_content +): + llm_model_value = "gpt-4o-mini" + messages_value = [{"role": "user", "content": "Say this is a test"}] + + response = await async_openai_client.chat.completions.create( + messages=messages_value, + model=llm_model_value, + seed=42, + temperature=0.5, + max_tokens=50, + stream=False, + extra_body={"service_tier": "default"}, + response_format={"type": "text"}, + ) + + spans = span_exporter.get_finished_spans() + assert_all_attributes( + spans[0], + llm_model_value, + response.id, + response.model, + response.usage.prompt_tokens, + response.usage.completion_tokens, + request_service_tier="default", + response_service_tier=getattr(response, "service_tier", None), + ) + 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" + ) + assert ( + spans[0].attributes[ + GenAIAttributes.GEN_AI_OPENAI_REQUEST_RESPONSE_FORMAT + ] + == "text" + ) + + +@pytest.mark.vcr() +@pytest.mark.asyncio() +async def test_async_chat_completion_multiple_choices( + span_exporter, log_exporter, async_openai_client, instrument_with_content +): + llm_model_value = "gpt-4o-mini" + messages_value = [{"role": "user", "content": "Say this is a test"}] + + response = await async_openai_client.chat.completions.create( + messages=messages_value, model=llm_model_value, n=2, stream=False + ) + + spans = span_exporter.get_finished_spans() + assert_all_attributes( + spans[0], + llm_model_value, + response.id, + response.model, + response.usage.prompt_tokens, + response.usage.completion_tokens, + ) + + logs = log_exporter.get_finished_logs() + assert len(logs) == 3 # 1 user message + 2 choice messages + + user_message = {"content": messages_value[0]["content"]} + assert_message_in_logs( + logs[0], "gen_ai.user.message", user_message, spans[0] + ) + + choice_event_0 = { + "index": 0, + "finish_reason": "stop", + "message": { + "role": "assistant", + "content": response.choices[0].message.content, + }, + } + assert_message_in_logs(logs[1], "gen_ai.choice", choice_event_0, spans[0]) + + choice_event_1 = { + "index": 1, + "finish_reason": "stop", + "message": { + "role": "assistant", + "content": response.choices[1].message.content, + }, + } + assert_message_in_logs(logs[2], "gen_ai.choice", choice_event_1, spans[0]) + + +@pytest.mark.vcr() +@pytest.mark.asyncio() +async def test_async_chat_completion_tool_calls_with_content( + span_exporter, log_exporter, async_openai_client, instrument_with_content +): + await chat_completion_tool_call( + span_exporter, log_exporter, async_openai_client, True + ) + + +@pytest.mark.vcr() +@pytest.mark.asyncio() +async def test_async_chat_completion_tool_calls_no_content( + span_exporter, log_exporter, async_openai_client, instrument_no_content +): + await chat_completion_tool_call( + span_exporter, log_exporter, async_openai_client, False + ) + + +async def chat_completion_tool_call( + span_exporter, log_exporter, async_openai_client, expect_content +): + llm_model_value = "gpt-4o-mini" + messages_value = [ + {"role": "system", "content": "You're a helpful assistant."}, + { + "role": "user", + "content": "What's the weather in Seattle and San Francisco today?", + }, + ] + + response_0 = await async_openai_client.chat.completions.create( + messages=messages_value, + model=llm_model_value, + tool_choice="auto", + tools=[get_current_weather_tool_definition()], + ) + + # sanity check + assert "tool_calls" in response_0.choices[0].finish_reason + + # final request + messages_value.append( + { + "role": "assistant", + "tool_calls": response_0.choices[0].message.to_dict()[ + "tool_calls" + ], + } + ) + + tool_call_result_0 = { + "role": "tool", + "content": "50 degrees and raining", + "tool_call_id": response_0.choices[0].message.tool_calls[0].id, + } + tool_call_result_1 = { + "role": "tool", + "content": "70 degrees and sunny", + "tool_call_id": response_0.choices[0].message.tool_calls[1].id, + } + + messages_value.append(tool_call_result_0) + messages_value.append(tool_call_result_1) + + response_1 = await async_openai_client.chat.completions.create( + messages=messages_value, model=llm_model_value + ) + + # sanity check + assert "stop" in response_1.choices[0].finish_reason + + # validate both calls + spans = span_exporter.get_finished_spans() + assert len(spans) == 2 + assert_all_attributes( + spans[0], + llm_model_value, + response_0.id, + response_0.model, + response_0.usage.prompt_tokens, + response_0.usage.completion_tokens, + ) + assert_all_attributes( + spans[1], + llm_model_value, + response_1.id, + response_1.model, + response_1.usage.prompt_tokens, + response_1.usage.completion_tokens, + ) + + logs = log_exporter.get_finished_logs() + assert len(logs) == 9 # 3 logs for first completion, 6 for second + + # call one + system_message = ( + {"content": messages_value[0]["content"]} if expect_content else None + ) + assert_message_in_logs( + logs[0], "gen_ai.system.message", system_message, spans[0] + ) + + user_message = ( + {"content": messages_value[1]["content"]} if expect_content else None + ) + assert_message_in_logs( + logs[1], "gen_ai.user.message", user_message, spans[0] + ) + + function_call_0 = {"name": "get_current_weather"} + function_call_1 = {"name": "get_current_weather"} + if expect_content: + function_call_0["arguments"] = ( + response_0.choices[0] + .message.tool_calls[0] + .function.arguments.replace("\n", "") + ) + function_call_1["arguments"] = ( + response_0.choices[0] + .message.tool_calls[1] + .function.arguments.replace("\n", "") + ) + + choice_event = { + "index": 0, + "finish_reason": "tool_calls", + "message": { + "role": "assistant", + "tool_calls": [ + { + "id": response_0.choices[0].message.tool_calls[0].id, + "type": "function", + "function": function_call_0, + }, + { + "id": response_0.choices[0].message.tool_calls[1].id, + "type": "function", + "function": function_call_1, + }, + ], + }, + } + assert_message_in_logs(logs[2], "gen_ai.choice", choice_event, spans[0]) + + # call two + system_message = ( + {"content": messages_value[0]["content"]} if expect_content else None + ) + assert_message_in_logs( + logs[3], "gen_ai.system.message", system_message, spans[1] + ) + + user_message = ( + {"content": messages_value[1]["content"]} if expect_content else None + ) + assert_message_in_logs( + logs[4], "gen_ai.user.message", user_message, spans[1] + ) + + assistant_tool_call = {"tool_calls": messages_value[2]["tool_calls"]} + if not expect_content: + assistant_tool_call["tool_calls"][0]["function"]["arguments"] = None + assistant_tool_call["tool_calls"][1]["function"]["arguments"] = None + + assert_message_in_logs( + logs[5], "gen_ai.assistant.message", assistant_tool_call, spans[1] + ) + + tool_message_0 = { + "id": tool_call_result_0["tool_call_id"], + "content": tool_call_result_0["content"] if expect_content else None, + } + + assert_message_in_logs( + logs[6], "gen_ai.tool.message", tool_message_0, spans[1] + ) + + tool_message_1 = { + "id": tool_call_result_1["tool_call_id"], + "content": tool_call_result_1["content"] if expect_content else None, + } + + assert_message_in_logs( + logs[7], "gen_ai.tool.message", tool_message_1, spans[1] + ) + + message = { + "role": "assistant", + "content": response_1.choices[0].message.content + if expect_content + else None, + } + choice = { + "index": 0, + "finish_reason": "stop", + "message": message, + } + assert_message_in_logs(logs[8], "gen_ai.choice", choice, spans[1]) + + +@pytest.mark.vcr() +@pytest.mark.asyncio() +async def test_async_chat_completion_streaming( + span_exporter, log_exporter, async_openai_client, instrument_with_content +): + llm_model_value = "gpt-4" + messages_value = [{"role": "user", "content": "Say this is a test"}] + + kwargs = { + "model": llm_model_value, + "messages": messages_value, + "stream": True, + "stream_options": {"include_usage": True}, + } + + response_stream_usage = None + response_stream_model = None + response_stream_id = None + response_stream_result = "" + response = await async_openai_client.chat.completions.create(**kwargs) + async for chunk in response: + if chunk.choices: + response_stream_result += chunk.choices[0].delta.content or "" + + # get the last chunk + if getattr(chunk, "usage", None): + response_stream_usage = chunk.usage + response_stream_model = chunk.model + response_stream_id = chunk.id + + spans = span_exporter.get_finished_spans() + assert_all_attributes( + spans[0], + llm_model_value, + response_stream_id, + response_stream_model, + response_stream_usage.prompt_tokens, + response_stream_usage.completion_tokens, + ) + + logs = log_exporter.get_finished_logs() + assert len(logs) == 2 + + user_message = {"content": "Say this is a test"} + assert_message_in_logs( + logs[0], "gen_ai.user.message", user_message, spans[0] + ) + + choice_event = { + "index": 0, + "finish_reason": "stop", + "message": {"role": "assistant", "content": response_stream_result}, + } + assert_message_in_logs(logs[1], "gen_ai.choice", choice_event, spans[0]) + + +@pytest.mark.vcr() +@pytest.mark.asyncio() +async def test_async_chat_completion_streaming_not_complete( + span_exporter, log_exporter, async_openai_client, instrument_with_content +): + llm_model_value = "gpt-4" + messages_value = [{"role": "user", "content": "Say this is a test"}] + + kwargs = { + "model": llm_model_value, + "messages": messages_value, + "stream": True, + } + + response_stream_model = None + response_stream_id = None + response_stream_result = "" + response = await async_openai_client.chat.completions.create(**kwargs) + idx = 0 + async for chunk in response: + if chunk.choices: + response_stream_result += chunk.choices[0].delta.content or "" + if idx == 1: + # fake a stop + break + + if chunk.model: + response_stream_model = chunk.model + if chunk.id: + response_stream_id = chunk.id + idx += 1 + + response.close() + spans = span_exporter.get_finished_spans() + assert_all_attributes( + spans[0], llm_model_value, response_stream_id, response_stream_model + ) + + logs = log_exporter.get_finished_logs() + assert len(logs) == 2 + + user_message = {"content": "Say this is a test"} + assert_message_in_logs( + logs[0], "gen_ai.user.message", user_message, spans[0] + ) + + choice_event = { + "index": 0, + "finish_reason": "error", + "message": {"role": "assistant", "content": response_stream_result}, + } + assert_message_in_logs(logs[1], "gen_ai.choice", choice_event, spans[0]) + + +@pytest.mark.vcr() +@pytest.mark.asyncio() +async def test_async_chat_completion_multiple_choices_streaming( + span_exporter, log_exporter, async_openai_client, instrument_with_content +): + llm_model_value = "gpt-4o-mini" + messages_value = [ + {"role": "system", "content": "You're a helpful assistant."}, + { + "role": "user", + "content": "What's the weather in Seattle and San Francisco today?", + }, + ] + + response_0 = await async_openai_client.chat.completions.create( + messages=messages_value, + model=llm_model_value, + n=2, + stream=True, + stream_options={"include_usage": True}, + ) + + # two strings for each choice + response_stream_result = ["", ""] + finish_reasons = ["", ""] + async for chunk in response_0: + if chunk.choices: + for choice in chunk.choices: + response_stream_result[choice.index] += ( + choice.delta.content or "" + ) + if choice.finish_reason: + finish_reasons[choice.index] = choice.finish_reason + + # get the last chunk + if getattr(chunk, "usage", None): + response_stream_usage = chunk.usage + response_stream_model = chunk.model + response_stream_id = chunk.id + + # sanity check + assert "stop" == finish_reasons[0] + + spans = span_exporter.get_finished_spans() + assert_all_attributes( + spans[0], + llm_model_value, + response_stream_id, + response_stream_model, + response_stream_usage.prompt_tokens, + response_stream_usage.completion_tokens, + ) + + logs = log_exporter.get_finished_logs() + assert len(logs) == 4 + + system_message = {"content": messages_value[0]["content"]} + assert_message_in_logs( + logs[0], "gen_ai.system.message", system_message, spans[0] + ) + + user_message = { + "content": "What's the weather in Seattle and San Francisco today?" + } + assert_message_in_logs( + logs[1], "gen_ai.user.message", user_message, spans[0] + ) + + choice_event_0 = { + "index": 0, + "finish_reason": "stop", + "message": { + "role": "assistant", + "content": "".join(response_stream_result[0]), + }, + } + assert_message_in_logs(logs[2], "gen_ai.choice", choice_event_0, spans[0]) + + choice_event_1 = { + "index": 1, + "finish_reason": "stop", + "message": { + "role": "assistant", + "content": "".join(response_stream_result[1]), + }, + } + assert_message_in_logs(logs[3], "gen_ai.choice", choice_event_1, spans[0]) + + +@pytest.mark.vcr() +@pytest.mark.asyncio() +async def test_async_chat_completion_multiple_tools_streaming_with_content( + span_exporter, log_exporter, async_openai_client, instrument_with_content +): + await async_chat_completion_multiple_tools_streaming( + span_exporter, log_exporter, async_openai_client, True + ) + + +@pytest.mark.vcr() +@pytest.mark.asyncio() +async def test_async_chat_completion_multiple_tools_streaming_no_content( + span_exporter, log_exporter, async_openai_client, instrument_no_content +): + await async_chat_completion_multiple_tools_streaming( + span_exporter, log_exporter, async_openai_client, False + ) + + +@pytest.mark.vcr() +@pytest.mark.asyncio() +async def test_async_chat_completion_streaming_unsampled( + span_exporter, + log_exporter, + async_openai_client, + instrument_with_content_unsampled, +): + llm_model_value = "gpt-4" + messages_value = [{"role": "user", "content": "Say this is a test"}] + + kwargs = { + "model": llm_model_value, + "messages": messages_value, + "stream": True, + "stream_options": {"include_usage": True}, + } + + response_stream_result = "" + response = await async_openai_client.chat.completions.create(**kwargs) + async for chunk in response: + if chunk.choices: + response_stream_result += chunk.choices[0].delta.content or "" + + spans = span_exporter.get_finished_spans() + assert len(spans) == 0 + + 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, None) + + 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, None) + + assert logs[0].log_record.trace_id is not None + assert logs[0].log_record.span_id is not None + assert logs[0].log_record.trace_flags == 0 + + assert logs[0].log_record.trace_id == logs[1].log_record.trace_id + assert logs[0].log_record.span_id == logs[1].log_record.span_id + assert logs[0].log_record.trace_flags == logs[1].log_record.trace_flags + + +async def async_chat_completion_multiple_tools_streaming( + span_exporter, log_exporter, async_openai_client, expect_content +): + llm_model_value = "gpt-4o-mini" + messages_value = [ + {"role": "system", "content": "You're a helpful assistant."}, + { + "role": "user", + "content": "What's the weather in Seattle and San Francisco today?", + }, + ] + + response = await async_openai_client.chat.completions.create( + messages=messages_value, + model=llm_model_value, + tool_choice="auto", + tools=[get_current_weather_tool_definition()], + stream=True, + stream_options={"include_usage": True}, + ) + + finish_reason = None + # two tools + tool_names = ["", ""] + tool_call_ids = ["", ""] + tool_args = ["", ""] + async for chunk in response: + if chunk.choices: + if chunk.choices[0].finish_reason: + finish_reason = chunk.choices[0].finish_reason + for tool_call in chunk.choices[0].delta.tool_calls or []: + t_idx = tool_call.index + if tool_call.id: + tool_call_ids[t_idx] = tool_call.id + if tool_call.function: + if tool_call.function.arguments: + tool_args[t_idx] += tool_call.function.arguments + if tool_call.function.name: + tool_names[t_idx] = tool_call.function.name + + # get the last chunk + if getattr(chunk, "usage", None): + response_stream_usage = chunk.usage + response_stream_model = chunk.model + response_stream_id = chunk.id + + # sanity check + assert "tool_calls" == finish_reason + + spans = span_exporter.get_finished_spans() + assert_all_attributes( + spans[0], + llm_model_value, + response_stream_id, + response_stream_model, + response_stream_usage.prompt_tokens, + response_stream_usage.completion_tokens, + ) + + logs = log_exporter.get_finished_logs() + assert len(logs) == 3 + + system_message = ( + {"content": messages_value[0]["content"]} if expect_content else None + ) + assert_message_in_logs( + logs[0], "gen_ai.system.message", system_message, spans[0] + ) + + user_message = ( + {"content": "What's the weather in Seattle and San Francisco today?"} + if expect_content + else None + ) + assert_message_in_logs( + logs[1], "gen_ai.user.message", user_message, spans[0] + ) + + choice_event = { + "index": 0, + "finish_reason": "tool_calls", + "message": { + "role": "assistant", + "tool_calls": [ + { + "id": tool_call_ids[0], + "type": "function", + "function": { + "name": tool_names[0], + "arguments": ( + tool_args[0].replace("\n", "") + if expect_content + else None + ), + }, + }, + { + "id": tool_call_ids[1], + "type": "function", + "function": { + "name": tool_names[1], + "arguments": ( + tool_args[1].replace("\n", "") + if expect_content + else None + ), + }, + }, + ], + }, + } + assert_message_in_logs(logs[2], "gen_ai.choice", choice_event, spans[0]) + + +def assert_message_in_logs(log, event_name, expected_content, parent_span): + assert log.log_record.event_name == event_name + assert ( + log.log_record.attributes[GenAIAttributes.GEN_AI_SYSTEM] + == GenAIAttributes.GenAiSystemValues.OPENAI.value + ) + + if not expected_content: + assert not log.log_record.body + else: + assert log.log_record.body + assert dict(log.log_record.body) == remove_none_values( + expected_content + ) + assert_log_parent(log, parent_span) + + +def 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, + }, + }, + } diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/test_async_embeddings.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/test_async_embeddings.py new file mode 100644 index 0000000..cfb2f5f --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/test_async_embeddings.py @@ -0,0 +1,241 @@ +# 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. + +"""Unit tests for OpenAI Async Embeddings API instrumentation.""" + +import pytest +from openai import AsyncOpenAI, NotFoundError + +from opentelemetry.sdk.trace import ReadableSpan +from opentelemetry.semconv._incubating.attributes import ( + error_attributes as ErrorAttributes, +) +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAIAttributes, +) +from opentelemetry.semconv._incubating.metrics import gen_ai_metrics + +from .test_utils import assert_all_attributes + + +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_async_embeddings_no_content( + span_exporter, log_exporter, async_openai_client, instrument_no_content +): + """Test creating embeddings asynchronously with content capture disabled""" + model_name = "text-embedding-3-small" + input_text = "This is a test for async embeddings" + + response = await async_openai_client.embeddings.create( + model=model_name, + input=input_text, + ) + + # Verify spans + spans = span_exporter.get_finished_spans() + assert len(spans) == 1 + assert_embedding_attributes(spans[0], model_name, response) + + # No logs should be emitted when content capture is disabled + logs = log_exporter.get_finished_logs() + assert len(logs) == 0 + + +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_async_embeddings_with_dimensions( + span_exporter, metric_reader, async_openai_client, instrument_no_content +): + """Test creating embeddings asynchronously with custom dimensions""" + model_name = "text-embedding-3-small" + input_text = "This is a test for async embeddings with dimensions" + dimensions = 512 # Using a smaller dimension than default + + response = await async_openai_client.embeddings.create( + model=model_name, + input=input_text, + dimensions=dimensions, + ) + + # Verify spans + spans = span_exporter.get_finished_spans() + assert len(spans) == 1 + assert_embedding_attributes(spans[0], model_name, response) + + # Verify dimensions attribute is set correctly + assert ( + spans[0].attributes["gen_ai.embeddings.dimension.count"] == dimensions + ) + + # Verify actual embedding dimensions match the requested dimensions + assert len(response.data[0].embedding) == dimensions + + +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_async_embeddings_with_batch_input( + span_exporter, metric_reader, async_openai_client, instrument_with_content +): + """Test creating embeddings asynchronously with batch input""" + model_name = "text-embedding-3-small" + input_texts = [ + "This is the first test string for async embeddings", + "This is the second test string for async embeddings", + "This is the third test string for async embeddings", + ] + + response = await async_openai_client.embeddings.create( + model=model_name, + input=input_texts, + ) + + # Verify spans + spans = span_exporter.get_finished_spans() + assert len(spans) == 1 + assert_embedding_attributes(spans[0], model_name, response) + + # Verify results contain the same number of embeddings as input texts + assert len(response.data) == len(input_texts) + + +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_async_embeddings_error_handling( + span_exporter, metric_reader, instrument_no_content +): + """Test async embeddings error handling""" + model_name = "non-existent-embedding-model" + input_text = "This is a test for async embeddings with error" + + client = AsyncOpenAI() + + with pytest.raises(NotFoundError): + await client.embeddings.create( + model=model_name, + input=input_text, + ) + + # Verify spans + spans = span_exporter.get_finished_spans() + assert len(spans) == 1 + assert_all_attributes(spans[0], model_name, operation_name="embeddings") + assert "NotFoundError" == spans[0].attributes[ErrorAttributes.ERROR_TYPE] + + +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_async_embeddings_token_metrics( + span_exporter, metric_reader, async_openai_client, instrument_no_content +): + """Test that token usage metrics are correctly recorded for async embeddings""" + model_name = "text-embedding-3-small" + input_text = "This is a test for async embeddings token metrics" + + response = await async_openai_client.embeddings.create( + model=model_name, + input=input_text, + ) + + # Verify spans + spans = span_exporter.get_finished_spans() + assert len(spans) == 1 + assert_embedding_attributes(spans[0], model_name, response) + + # Verify metrics + metrics = metric_reader.get_metrics_data().resource_metrics + assert len(metrics) == 1 + + metric_data = metrics[0].scope_metrics[0].metrics + + # Verify token usage metric + token_metric = next( + ( + m + for m in metric_data + if m.name == gen_ai_metrics.GEN_AI_CLIENT_TOKEN_USAGE + ), + None, + ) + assert token_metric is not None + + # Find the input token data point + input_token_point = None + for point in token_metric.data.data_points: + if ( + point.attributes[GenAIAttributes.GEN_AI_TOKEN_TYPE] + == GenAIAttributes.GenAiTokenTypeValues.INPUT.value + ): + input_token_point = point + break + + assert input_token_point is not None, "Input token metric not found" + + # Verify the token counts match what was reported in the response + assert input_token_point.sum == response.usage.prompt_tokens + + +@pytest.mark.asyncio +@pytest.mark.vcr() +async def test_async_embeddings_with_encoding_format( + span_exporter, metric_reader, async_openai_client, instrument_no_content +): + """Test creating embeddings with different encoding format""" + model_name = "text-embedding-3-small" + input_text = "This is a test for embeddings with encoding format" + encoding_format = "base64" + + response = await async_openai_client.embeddings.create( + model=model_name, + input=input_text, + encoding_format=encoding_format, + ) + + # Verify spans + spans = span_exporter.get_finished_spans() + assert len(spans) == 1 + assert_embedding_attributes(spans[0], model_name, response) + + # Verify encoding_format attribute is set correctly + assert spans[0].attributes["gen_ai.request.encoding_formats"] == ( + encoding_format, + ) + + +def assert_embedding_attributes( + span: ReadableSpan, + request_model: str, + response, +): + """Assert that the span contains all required attributes for embeddings operation""" + # Use the common assertion function + assert_all_attributes( + span, + request_model, + response_id=None, # Embeddings don't have a response ID + response_model=response.model, + input_tokens=response.usage.prompt_tokens, + operation_name="embeddings", + server_address="api.openai.com", + ) + + # Assert embeddings-specific attributes + if ( + hasattr(span, "attributes") + and "gen_ai.embeddings.dimension.count" in span.attributes + ): + # If dimensions were specified, verify that they match the actual dimensions + assert span.attributes["gen_ai.embeddings.dimension.count"] == len( + response.data[0].embedding + ) diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/test_chat_completions.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/test_chat_completions.py new file mode 100644 index 0000000..ce54b96 --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/test_chat_completions.py @@ -0,0 +1,946 @@ +# 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 + +import logging + +import pytest +from openai import ( + NOT_GIVEN, + APIConnectionError, + NotFoundError, + OpenAI, +) + +try: + from openai import not_given # pylint: disable=no-name-in-module +except ImportError: + not_given = NOT_GIVEN + +from opentelemetry.semconv._incubating.attributes import ( + error_attributes as ErrorAttributes, +) +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAIAttributes, +) +from opentelemetry.semconv._incubating.attributes import ( + server_attributes as ServerAttributes, +) +from opentelemetry.semconv._incubating.metrics import gen_ai_metrics + +from .test_utils import ( + assert_all_attributes, + assert_log_parent, + remove_none_values, +) + + +@pytest.mark.vcr() +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"}] + + response = openai_client.chat.completions.create( + messages=messages_value, + model=llm_model_value, + stream=False, + ) + + spans = span_exporter.get_finished_spans() + assert_all_attributes( + spans[0], + llm_model_value, + response.id, + response.model, + response.usage.prompt_tokens, + response.usage.completion_tokens, + ) + + logs = log_exporter.get_finished_logs() + assert len(logs) == 2 + + user_message = {"content": messages_value[0]["content"]} + assert_message_in_logs( + logs[0], "gen_ai.user.message", user_message, spans[0] + ) + + choice_event = { + "index": 0, + "finish_reason": "stop", + "message": { + "role": "assistant", + "content": response.choices[0].message.content, + }, + } + assert_message_in_logs(logs[1], "gen_ai.choice", choice_event, spans[0]) + + +@pytest.mark.vcr() +def test_chat_completion_handles_not_given( + span_exporter, log_exporter, openai_client, instrument_no_content, caplog +): + caplog.set_level(logging.WARNING) + 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, + stream=False, + top_p=NOT_GIVEN, + max_tokens=not_given, + ) + + (span,) = span_exporter.get_finished_spans() + assert_all_attributes( + span, + llm_model_value, + response.id, + response.model, + response.usage.prompt_tokens, + response.usage.completion_tokens, + response_service_tier="default", + ) + + assert GenAIAttributes.GEN_AI_REQUEST_TOP_P not in span.attributes + assert GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS not in span.attributes + + logs = log_exporter.get_finished_logs() + assert len(logs) == 2 + + assert_no_invalid_type_warning(caplog) + + +@pytest.mark.vcr() +def test_chat_completion_no_content( + span_exporter, log_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, stream=False + ) + + spans = span_exporter.get_finished_spans() + assert_all_attributes( + spans[0], + llm_model_value, + response.id, + response.model, + response.usage.prompt_tokens, + response.usage.completion_tokens, + ) + + logs = log_exporter.get_finished_logs() + assert len(logs) == 2 + + assert_message_in_logs(logs[0], "gen_ai.user.message", None, spans[0]) + + choice_event = { + "index": 0, + "finish_reason": "stop", + "message": {"role": "assistant"}, + } + assert_message_in_logs(logs[1], "gen_ai.choice", choice_event, spans[0]) + + +def test_chat_completion_bad_endpoint( + span_exporter, metric_reader, instrument_no_content +): + llm_model_value = "gpt-4o-mini" + messages_value = [{"role": "user", "content": "Say this is a test"}] + + client = OpenAI(base_url="http://localhost:4242") + + with pytest.raises(APIConnectionError): + client.chat.completions.create( + messages=messages_value, + model=llm_model_value, + timeout=0.1, + ) + + spans = span_exporter.get_finished_spans() + assert_all_attributes( + spans[0], llm_model_value, server_address="localhost" + ) + assert 4242 == spans[0].attributes[ServerAttributes.SERVER_PORT] + assert ( + "APIConnectionError" == spans[0].attributes[ErrorAttributes.ERROR_TYPE] + ) + + metrics = metric_reader.get_metrics_data().resource_metrics + assert len(metrics) == 1 + + metric_data = metrics[0].scope_metrics[0].metrics + duration_metric = next( + ( + m + for m in metric_data + if m.name == gen_ai_metrics.GEN_AI_CLIENT_OPERATION_DURATION + ), + None, + ) + assert duration_metric is not None + assert duration_metric.data.data_points[0].sum > 0 + assert ( + duration_metric.data.data_points[0].attributes[ + ErrorAttributes.ERROR_TYPE + ] + == "APIConnectionError" + ) + + +@pytest.mark.vcr() +def test_chat_completion_404( + span_exporter, openai_client, metric_reader, instrument_no_content +): + llm_model_value = "this-model-does-not-exist" + messages_value = [{"role": "user", "content": "Say this is a test"}] + + with pytest.raises(NotFoundError): + openai_client.chat.completions.create( + messages=messages_value, + model=llm_model_value, + ) + + spans = span_exporter.get_finished_spans() + + assert_all_attributes(spans[0], llm_model_value) + assert "NotFoundError" == spans[0].attributes[ErrorAttributes.ERROR_TYPE] + + metrics = metric_reader.get_metrics_data().resource_metrics + assert len(metrics) == 1 + + metric_data = metrics[0].scope_metrics[0].metrics + duration_metric = next( + ( + m + for m in metric_data + if m.name == gen_ai_metrics.GEN_AI_CLIENT_OPERATION_DURATION + ), + None, + ) + assert duration_metric is not None + assert duration_metric.data.data_points[0].sum > 0 + assert ( + duration_metric.data.data_points[0].attributes[ + ErrorAttributes.ERROR_TYPE + ] + == "NotFoundError" + ) + + +@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"}, + response_format={"type": "text"}, + ) + + spans = span_exporter.get_finished_spans() + assert_all_attributes( + spans[0], + llm_model_value, + response.id, + response.model, + response.usage.prompt_tokens, + response.usage.completion_tokens, + request_service_tier="default", + response_service_tier=getattr(response, "service_tier", None), + ) + 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" + ) + assert ( + spans[0].attributes[ + GenAIAttributes.GEN_AI_OPENAI_REQUEST_RESPONSE_FORMAT + ] + == "text" + ) + + +@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_all_attributes( + spans[0], + llm_model_value, + response.id, + response.model, + response.usage.prompt_tokens, + response.usage.completion_tokens, + ) + + 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_all_attributes( + spans[0], + llm_model_value, + response_0.id, + response_0.model, + response_0.usage.prompt_tokens, + response_0.usage.completion_tokens, + ) + assert_all_attributes( + spans[1], + llm_model_value, + response_1.id, + response_1.model, + response_1.usage.prompt_tokens, + response_1.usage.completion_tokens, + ) + + 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"}] + + kwargs = { + "model": llm_model_value, + "messages": messages_value, + "stream": True, + "stream_options": {"include_usage": True}, + } + + response_stream_usage = None + response_stream_model = None + response_stream_id = None + response_stream_result = "" + response = openai_client.chat.completions.create(**kwargs) + for chunk in response: + if chunk.choices: + response_stream_result += chunk.choices[0].delta.content or "" + + # get the last chunk + if getattr(chunk, "usage", None): + response_stream_usage = chunk.usage + response_stream_model = chunk.model + response_stream_id = chunk.id + + spans = span_exporter.get_finished_spans() + assert_all_attributes( + spans[0], + llm_model_value, + response_stream_id, + response_stream_model, + response_stream_usage.prompt_tokens, + response_stream_usage.completion_tokens, + ) + + logs = log_exporter.get_finished_logs() + assert len(logs) == 2 + + user_message = {"content": "Say this is a test"} + assert_message_in_logs( + logs[0], "gen_ai.user.message", user_message, spans[0] + ) + + choice_event = { + "index": 0, + "finish_reason": "stop", + "message": {"role": "assistant", "content": response_stream_result}, + } + assert_message_in_logs(logs[1], "gen_ai.choice", choice_event, spans[0]) + + +@pytest.mark.vcr() +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 + ) + + +@pytest.mark.vcr() +def test_chat_completion_with_content_span_unsampled( + span_exporter, + log_exporter, + openai_client, + instrument_with_content_unsampled, +): + 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, stream=False + ) + + spans = span_exporter.get_finished_spans() + assert len(spans) == 0 + + 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, None) + + 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, None) + + assert logs[0].log_record.trace_id is not None + assert logs[0].log_record.span_id is not None + assert logs[0].log_record.trace_flags == 0 + + assert logs[0].log_record.trace_id == logs[1].log_record.trace_id + assert logs[0].log_record.span_id == logs[1].log_record.span_id + assert logs[0].log_record.trace_flags == logs[1].log_record.trace_flags + + +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.event_name == event_name + assert ( + log.log_record.attributes[GenAIAttributes.GEN_AI_SYSTEM] + == GenAIAttributes.GenAiSystemValues.OPENAI.value + ) + + if not expected_content: + assert not log.log_record.body + else: + assert log.log_record.body + assert dict(log.log_record.body) == remove_none_values( + expected_content + ) + assert_log_parent(log, parent_span) + + +def 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, + }, + }, + } + + +def assert_no_invalid_type_warning(caplog): + assert "Invalid type" not in caplog.text diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/test_chat_metrics.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/test_chat_metrics.py new file mode 100644 index 0000000..ffcd99c --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/test_chat_metrics.py @@ -0,0 +1,227 @@ +import pytest + +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAIAttributes, +) +from opentelemetry.semconv._incubating.attributes import ( + server_attributes as ServerAttributes, +) +from opentelemetry.semconv._incubating.metrics import gen_ai_metrics + +_DURATION_BUCKETS = ( + 0.01, + 0.02, + 0.04, + 0.08, + 0.16, + 0.32, + 0.64, + 1.28, + 2.56, + 5.12, + 10.24, + 20.48, + 40.96, + 81.92, +) +_TOKEN_USAGE_BUCKETS = ( + 1, + 4, + 16, + 64, + 256, + 1024, + 4096, + 16384, + 65536, + 262144, + 1048576, + 4194304, + 16777216, + 67108864, +) + + +def assert_all_metric_attributes(data_point): + assert GenAIAttributes.GEN_AI_OPERATION_NAME in data_point.attributes + assert ( + data_point.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME] + == GenAIAttributes.GenAiOperationNameValues.CHAT.value + ) + assert GenAIAttributes.GEN_AI_SYSTEM in data_point.attributes + assert ( + data_point.attributes[GenAIAttributes.GEN_AI_SYSTEM] + == GenAIAttributes.GenAiSystemValues.OPENAI.value + ) + assert GenAIAttributes.GEN_AI_REQUEST_MODEL in data_point.attributes + assert ( + data_point.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] + == "gpt-4o-mini" + ) + assert GenAIAttributes.GEN_AI_RESPONSE_MODEL in data_point.attributes + assert ( + data_point.attributes[GenAIAttributes.GEN_AI_RESPONSE_MODEL] + == "gpt-4o-mini-2024-07-18" + ) + assert "gen_ai.openai.response.system_fingerprint" in data_point.attributes + assert ( + data_point.attributes["gen_ai.openai.response.system_fingerprint"] + == "fp_0ba0d124f1" + ) + assert ( + GenAIAttributes.GEN_AI_OPENAI_RESPONSE_SERVICE_TIER + in data_point.attributes + ) + assert ( + data_point.attributes[ + GenAIAttributes.GEN_AI_OPENAI_RESPONSE_SERVICE_TIER + ] + == "default" + ) + assert ( + data_point.attributes[ServerAttributes.SERVER_ADDRESS] + == "api.openai.com" + ) + + +@pytest.mark.vcr() +def test_chat_completion_metrics( + metric_reader, openai_client, instrument_with_content +): + llm_model_value = "gpt-4o-mini" + messages_value = [{"role": "user", "content": "Say this is a test"}] + + openai_client.chat.completions.create( + messages=messages_value, model=llm_model_value, stream=False + ) + + metrics = metric_reader.get_metrics_data().resource_metrics + assert len(metrics) == 1 + + metric_data = metrics[0].scope_metrics[0].metrics + assert len(metric_data) == 2 + + duration_metric = next( + ( + m + for m in metric_data + if m.name == gen_ai_metrics.GEN_AI_CLIENT_OPERATION_DURATION + ), + None, + ) + assert duration_metric is not None + + duration_point = duration_metric.data.data_points[0] + assert duration_point.sum > 0 + assert_all_metric_attributes(duration_point) + assert duration_point.explicit_bounds == _DURATION_BUCKETS + + token_usage_metric = next( + ( + m + for m in metric_data + if m.name == gen_ai_metrics.GEN_AI_CLIENT_TOKEN_USAGE + ), + None, + ) + assert token_usage_metric is not None + + input_token_usage = next( + ( + d + for d in token_usage_metric.data.data_points + if d.attributes[GenAIAttributes.GEN_AI_TOKEN_TYPE] + == GenAIAttributes.GenAiTokenTypeValues.INPUT.value + ), + None, + ) + assert input_token_usage is not None + assert input_token_usage.sum == 12 + + assert input_token_usage.explicit_bounds == _TOKEN_USAGE_BUCKETS + assert input_token_usage.bucket_counts[2] == 1 + assert_all_metric_attributes(input_token_usage) + + output_token_usage = next( + ( + d + for d in token_usage_metric.data.data_points + if d.attributes[GenAIAttributes.GEN_AI_TOKEN_TYPE] + == GenAIAttributes.GenAiTokenTypeValues.COMPLETION.value + ), + None, + ) + assert output_token_usage is not None + assert output_token_usage.sum == 5 + # assert against buckets [1, 4, 16, 64, 256, 1024, 4096, 16384, 65536, 262144, 1048576, 4194304, 16777216, 67108864] + assert output_token_usage.bucket_counts[2] == 1 + assert_all_metric_attributes(output_token_usage) + + +@pytest.mark.vcr() +@pytest.mark.asyncio() +async def test_async_chat_completion_metrics( + metric_reader, async_openai_client, instrument_with_content +): + llm_model_value = "gpt-4o-mini" + messages_value = [{"role": "user", "content": "Say this is a test"}] + + await async_openai_client.chat.completions.create( + messages=messages_value, model=llm_model_value, stream=False + ) + + metrics = metric_reader.get_metrics_data().resource_metrics + assert len(metrics) == 1 + + metric_data = metrics[0].scope_metrics[0].metrics + assert len(metric_data) == 2 + + duration_metric = next( + ( + m + for m in metric_data + if m.name == gen_ai_metrics.GEN_AI_CLIENT_OPERATION_DURATION + ), + None, + ) + assert duration_metric is not None + assert duration_metric.data.data_points[0].sum > 0 + assert_all_metric_attributes(duration_metric.data.data_points[0]) + + token_usage_metric = next( + ( + m + for m in metric_data + if m.name == gen_ai_metrics.GEN_AI_CLIENT_TOKEN_USAGE + ), + None, + ) + assert token_usage_metric is not None + + input_token_usage = next( + ( + d + for d in token_usage_metric.data.data_points + if d.attributes[GenAIAttributes.GEN_AI_TOKEN_TYPE] + == GenAIAttributes.GenAiTokenTypeValues.INPUT.value + ), + None, + ) + + assert input_token_usage is not None + assert input_token_usage.sum == 12 + assert_all_metric_attributes(input_token_usage) + + output_token_usage = next( + ( + d + for d in token_usage_metric.data.data_points + if d.attributes[GenAIAttributes.GEN_AI_TOKEN_TYPE] + == GenAIAttributes.GenAiTokenTypeValues.COMPLETION.value + ), + None, + ) + + assert output_token_usage is not None + assert output_token_usage.sum == 12 + assert_all_metric_attributes(output_token_usage) diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/test_embeddings.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/test_embeddings.py new file mode 100644 index 0000000..184d372 --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/test_embeddings.py @@ -0,0 +1,386 @@ +# 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. + +"""Unit tests for OpenAI Embeddings API instrumentation.""" + +import pytest +from openai import ( + NOT_GIVEN, + APIConnectionError, + NotFoundError, + OpenAI, +) + +try: + from openai import not_given # pylint: disable=no-name-in-module +except ImportError: + not_given = NOT_GIVEN + +from opentelemetry.sdk.trace import ReadableSpan +from opentelemetry.semconv._incubating.attributes import ( + error_attributes as ErrorAttributes, +) +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAIAttributes, +) +from opentelemetry.semconv._incubating.attributes import ( + server_attributes as ServerAttributes, +) +from opentelemetry.semconv._incubating.metrics import gen_ai_metrics + +from .test_utils import assert_all_attributes + + +@pytest.mark.vcr() +def test_embeddings_no_content( + span_exporter, log_exporter, openai_client, instrument_no_content +): + """Test creating embeddings with content capture disabled""" + model_name = "text-embedding-3-small" + input_text = "This is a test for embeddings" + + response = openai_client.embeddings.create( + model=model_name, + input=input_text, + ) + + # Verify spans + spans = span_exporter.get_finished_spans() + assert len(spans) == 1 + assert_embedding_attributes(spans[0], model_name, response) + + # No logs should be emitted when content capture is disabled + logs = log_exporter.get_finished_logs() + assert len(logs) == 0 + + +@pytest.mark.vcr() +def test_embeddings_with_dimensions( + span_exporter, metric_reader, openai_client, instrument_no_content +): + """Test creating embeddings with custom dimensions parameter""" + model_name = "text-embedding-3-small" + input_text = "This is a test for embeddings with dimensions" + dimensions = 512 # Using a smaller dimension than default + + response = openai_client.embeddings.create( + model=model_name, + input=input_text, + dimensions=dimensions, + ) + + # Verify spans + spans = span_exporter.get_finished_spans() + assert len(spans) == 1 + assert_embedding_attributes(spans[0], model_name, response) + + # Verify dimensions attribute is set correctly + assert ( + spans[0].attributes["gen_ai.embeddings.dimension.count"] == dimensions + ) + + # Verify actual embedding dimensions match the requested dimensions + assert len(response.data[0].embedding) == dimensions + + # Verify metrics + metrics = metric_reader.get_metrics_data().resource_metrics + assert len(metrics) == 1 + + metric_data = metrics[0].scope_metrics[0].metrics + duration_metric = next( + ( + m + for m in metric_data + if m.name == gen_ai_metrics.GEN_AI_CLIENT_OPERATION_DURATION + ), + None, + ) + assert duration_metric is not None + + # Verify the dimensions attribute is present in metrics + for point in duration_metric.data.data_points: + if "gen_ai.embeddings.dimension.count" in point.attributes: + assert ( + point.attributes["gen_ai.embeddings.dimension.count"] + == dimensions + ) + break + else: + assert False, "Dimensions attribute not found in metrics" + + +@pytest.mark.vcr() +def test_embeddings_with_batch_input( + span_exporter, metric_reader, openai_client, instrument_with_content +): + """Test creating embeddings with batch input (list of strings)""" + model_name = "text-embedding-3-small" + input_texts = [ + "This is the first test string for embeddings", + "This is the second test string for embeddings", + "This is the third test string for embeddings", + ] + + response = openai_client.embeddings.create( + model=model_name, + input=input_texts, + ) + + # Verify spans + spans = span_exporter.get_finished_spans() + assert len(spans) == 1 + assert_embedding_attributes(spans[0], model_name, response) + + # Verify results contain the same number of embeddings as input texts + assert len(response.data) == len(input_texts) + + +@pytest.mark.vcr() +def test_embeddings_with_encoding_format( + span_exporter, metric_reader, openai_client, instrument_no_content +): + """Test creating embeddings with different encoding format""" + model_name = "text-embedding-3-small" + input_text = "This is a test for embeddings with encoding format" + encoding_format = "base64" + + response = openai_client.embeddings.create( + model=model_name, + input=input_text, + encoding_format=encoding_format, + ) + + # Verify spans + spans = span_exporter.get_finished_spans() + assert len(spans) == 1 + assert_embedding_attributes(spans[0], model_name, response) + + # Verify encoding_format attribute is set correctly + assert spans[0].attributes["gen_ai.request.encoding_formats"] == ( + encoding_format, + ) + + +@pytest.mark.parametrize("not_given_value", [NOT_GIVEN, not_given]) +@pytest.mark.vcr() +def test_embeddings_with_not_given_values( + span_exporter, + metric_reader, + openai_client, + instrument_no_content, + not_given_value, +): + """Test creating embeddings with NOT_GIVEN and not_given values""" + model_name = "text-embedding-3-small" + input_text = "This is a test for embeddings with encoding format" + + response = openai_client.embeddings.create( + model=model_name, + input=input_text, + dimensions=not_given_value, + ) + + # Verify spans + spans = span_exporter.get_finished_spans() + assert len(spans) == 1 + assert_embedding_attributes(spans[0], model_name, response) + + assert "gen_ai.request.dimensions" not in spans[0].attributes + + +@pytest.mark.vcr() +def test_embeddings_bad_endpoint( + span_exporter, metric_reader, instrument_no_content +): + """Test error handling for bad endpoint""" + model_name = "text-embedding-3-small" + input_text = "This is a test for embeddings with bad endpoint" + + client = OpenAI(base_url="http://localhost:4242") + + with pytest.raises(APIConnectionError): + client.embeddings.create( + model=model_name, + input=input_text, + timeout=0.1, + ) + + # Verify spans + spans = span_exporter.get_finished_spans() + assert len(spans) == 1 + assert_all_attributes( + spans[0], + model_name, + operation_name="embeddings", + server_address="localhost", + ) + assert 4242 == spans[0].attributes[ServerAttributes.SERVER_PORT] + assert ( + "APIConnectionError" == spans[0].attributes[ErrorAttributes.ERROR_TYPE] + ) + + # Verify metrics + metrics = metric_reader.get_metrics_data().resource_metrics + assert len(metrics) == 1 + + metric_data = metrics[0].scope_metrics[0].metrics + duration_metric = next( + ( + m + for m in metric_data + if m.name == gen_ai_metrics.GEN_AI_CLIENT_OPERATION_DURATION + ), + None, + ) + assert duration_metric is not None + assert duration_metric.data.data_points[0].sum > 0 + assert ( + duration_metric.data.data_points[0].attributes[ + ErrorAttributes.ERROR_TYPE + ] + == "APIConnectionError" + ) + + +@pytest.mark.vcr() +def test_embeddings_model_not_found( + span_exporter, metric_reader, openai_client, instrument_no_content +): + """Test error handling for non-existent model""" + model_name = "non-existent-embedding-model" + input_text = "This is a test for embeddings with bad model" + + with pytest.raises(NotFoundError): + openai_client.embeddings.create( + model=model_name, + input=input_text, + ) + + # Verify spans + spans = span_exporter.get_finished_spans() + assert len(spans) == 1 + assert_all_attributes(spans[0], model_name, operation_name="embeddings") + assert "NotFoundError" == spans[0].attributes[ErrorAttributes.ERROR_TYPE] + + # Verify metrics + metrics = metric_reader.get_metrics_data().resource_metrics + assert len(metrics) == 1 + + metric_data = metrics[0].scope_metrics[0].metrics + duration_metric = next( + ( + m + for m in metric_data + if m.name == gen_ai_metrics.GEN_AI_CLIENT_OPERATION_DURATION + ), + None, + ) + assert duration_metric is not None + assert duration_metric.data.data_points[0].sum > 0 + assert ( + duration_metric.data.data_points[0].attributes[ + ErrorAttributes.ERROR_TYPE + ] + == "NotFoundError" + ) + + +@pytest.mark.vcr() +def test_embeddings_token_metrics( + span_exporter, metric_reader, openai_client, instrument_no_content +): + """Test that token usage metrics are correctly recorded""" + model_name = "text-embedding-3-small" + input_text = "This is a test for embeddings token metrics" + + response = openai_client.embeddings.create( + model=model_name, + input=input_text, + ) + + # Verify spans + spans = span_exporter.get_finished_spans() + assert len(spans) == 1 + assert_embedding_attributes(spans[0], model_name, response) + + # Verify metrics + metrics = metric_reader.get_metrics_data().resource_metrics + assert len(metrics) == 1 + + metric_data = metrics[0].scope_metrics[0].metrics + + # Verify operation duration metric + duration_metric = next( + ( + m + for m in metric_data + if m.name == gen_ai_metrics.GEN_AI_CLIENT_OPERATION_DURATION + ), + None, + ) + assert duration_metric is not None + + # Verify token usage metric + token_metric = next( + ( + m + for m in metric_data + if m.name == gen_ai_metrics.GEN_AI_CLIENT_TOKEN_USAGE + ), + None, + ) + assert token_metric is not None + + # Find the input token data point + input_token_point = None + for point in token_metric.data.data_points: + if ( + point.attributes[GenAIAttributes.GEN_AI_TOKEN_TYPE] + == GenAIAttributes.GenAiTokenTypeValues.INPUT.value + ): + input_token_point = point + break + + assert input_token_point is not None, "Input token metric not found" + + # Verify the token counts match what was reported in the response + assert input_token_point.sum == response.usage.prompt_tokens + + +def assert_embedding_attributes( + span: ReadableSpan, + request_model: str, + response, +): + """Assert that the span contains all required attributes for embeddings operation""" + # Use the common assertion function + assert_all_attributes( + span, + request_model, + response_id=None, # Embeddings don't have a response ID + response_model=response.model, + input_tokens=response.usage.prompt_tokens, + operation_name="embeddings", + server_address="api.openai.com", + ) + + # Assert embeddings-specific attributes + if ( + hasattr(span, "attributes") + and "gen_ai.embeddings.dimension.count" in span.attributes + ): + # If dimensions were specified, verify that they match the actual dimensions + assert span.attributes["gen_ai.embeddings.dimension.count"] == len( + response.data[0].embedding + ) diff --git a/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/test_utils.py b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/test_utils.py new file mode 100644 index 0000000..d4d64c4 --- /dev/null +++ b/instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/test_utils.py @@ -0,0 +1,117 @@ +# 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. + +"""Shared test utilities for OpenAI instrumentation tests.""" + +from typing import Optional + +from opentelemetry.sdk.trace import ReadableSpan +from opentelemetry.semconv._incubating.attributes import ( + gen_ai_attributes as GenAIAttributes, +) +from opentelemetry.semconv._incubating.attributes import ( + server_attributes as ServerAttributes, +) + + +def _assert_optional_attribute(span, attribute_name, expected_value): + """Helper to assert optional span attributes.""" + if expected_value is not None: + assert expected_value == span.attributes[attribute_name] + else: + assert attribute_name not in span.attributes + + +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", + server_port: int = 443, + request_service_tier: Optional[str] = None, + response_service_tier: Optional[str] = None, +): + assert span.name == f"{operation_name} {request_model}" + assert ( + operation_name + == span.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME] + ) + assert ( + GenAIAttributes.GenAiSystemValues.OPENAI.value + == span.attributes[GenAIAttributes.GEN_AI_SYSTEM] + ) + assert ( + request_model == span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] + ) + + _assert_optional_attribute( + span, GenAIAttributes.GEN_AI_RESPONSE_MODEL, response_model + ) + _assert_optional_attribute( + span, GenAIAttributes.GEN_AI_RESPONSE_ID, response_id + ) + _assert_optional_attribute( + span, GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS, input_tokens + ) + _assert_optional_attribute( + span, GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS, output_tokens + ) + + assert server_address == span.attributes[ServerAttributes.SERVER_ADDRESS] + + if server_port != 443 and server_port > 0: + assert server_port == span.attributes[ServerAttributes.SERVER_PORT] + + _assert_optional_attribute( + span, + GenAIAttributes.GEN_AI_OPENAI_REQUEST_SERVICE_TIER, + request_service_tier, + ) + _assert_optional_attribute( + span, + GenAIAttributes.GEN_AI_OPENAI_RESPONSE_SERVICE_TIER, + response_service_tier, + ) + + +def assert_log_parent(log, span): + """Assert that the log record has the correct parent span context""" + if 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 remove_none_values(body): + """Remove None values from a dictionary recursively""" + 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) if isinstance(i, dict) else i + for i in value + ] + else: + result[key] = value + return result