From 5c062d99fed7575a911df3492fef887f8a5d83ef Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Fri, 25 Oct 2024 15:46:27 +0900 Subject: [PATCH 01/32] Initial draft --- .../en/blog/2024/otel-generative-ai/index.md | 64 +++++++++++++++++++ 1 file changed, 64 insertions(+) create mode 100644 content/en/blog/2024/otel-generative-ai/index.md diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md new file mode 100644 index 000000000000..2f2723c865a8 --- /dev/null +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -0,0 +1,64 @@ +--- +title: OpenTelemetry for Generative AI +linkTitle: OpenTelemetry for Generative AI +date: 2024-11-01 +author: >- + [Drew Robbins](https://github.com/drewby) (Microsoft), [Luidmila + Molkova](https://github.com/lmolkova) (Microsoft) +--- + +As organizations increasingly adopt Large Language Models (LLMs) and other generative AI technologies, ensuring reliable performance, efficiency, and safety is essential. Enhanced observability—tracking AI operations, behaviors, and outcomes—helps meet these goals. OpenTelemetry is being enhanced to support these needs specifically for generative AI. + +Two primary assets are in development to make this possible: **Semantic Conventions** and an **Instrumentation Library**. + +**Semantic Conventions** establish standardized guidelines for how telemetry data is structured and collected across platforms, defining inputs, outputs, and operational details. For generative AI, these conventions streamline monitoring, troubleshooting, and optimizing AI models by standardizing attributes such as model parameters, response metadata, and token usage. This consistency supports better observability across tools, environments, and APIs, helping organizations track performance, cost, and safety with ease. + +The **Instrumentation Library** is being developed within the OpenTelemetry Python Contrib project to automate telemetry collection for generative AI applications. The first release is a Python library, given Python’s widespread use in AI development. Designed to integrate seamlessly with OpenAI’s API, this library captures spans, metrics, and events, gathering essential data like model inputs, response metadata, and token usage in a structured format. + +## Key Signals for Generative AI + +The [Semantic Conventions for Generative AI](https://github.com/open-telemetry/semantic-conventions/tree/main/docs/gen-ai) focus on capturing insights into AI model behavior through three primary signals: Spans, Metrics, and Events. + +**Spans: Tracing Model Interactions** +Spans track each model interaction’s lifecycle, covering input parameters (e.g., temperature, top_p) and response details like token count or errors. They provide visibility into each request, aiding in identifying bottlenecks and analyzing the impact of settings on model output. + +**Metrics: Monitoring Usage and Performance** +Metrics aggregate high-level indicators like request volume, latency, and resource use, essential for managing costs and performance. This data is particularly critical for API-dependent AI applications with rate limits and cost considerations. + +**Events: Capturing Detailed Interactions** +Events log detailed moments during model execution, such as user prompts and model responses, providing a granular view of model interactions. These insights are invaluable for debugging and optimizing AI applications where unexpected behaviors may arise. + +Together, these signals provide a comprehensive monitoring framework, enabling better cost management, performance tuning, and request tracing. + +**Extending Observability with Vendor-Specific Attributes** + +The Semantic Conventions also define vendor-specific attributes for platforms like OpenAI and Azure Inference API, ensuring telemetry captures both general and provider-specific details. This added flexibility supports multi-platform monitoring and in-depth insights. + +## Building the Python Instrumentation Library for OpenAI + +This Python-based library for OpenTelemetry captures key telemetry signals for OpenAI models, providing developers with an out-of-the-box observability solution tailored to AI workloads. The library, [hosted within the OpenTelemetry Contrib project](https://github.com/open-telemetry/opentelemetry-python-contrib/tree/main/instrumentation/opentelemetry-instrumentation-openai-v2), automatically collects telemetry from OpenAI model interactions, including request and response metadata, token usage, and operational metrics. + +As generative AI applications grow, additional instrumentation libraries for other languages will follow, extending OpenTelemetry support across more tools and environments. The current library’s focus on OpenAI highlights its popularity and demand within AI development, making it a valuable initial implementation. + +### Example Usage + +Here’s an example of using the OpenTelemetry Python library to monitor a generative AI application with the OpenAI client: + +```python +from openai import OpenAI +from opentelemetry.instrumentation.openai import OpenAIInstrumentor + +OpenAIInstrumentor().instrument() + +client = OpenAI() +response = client.chat.completions.create( + model="gpt-4-mini", + messages=[{"role": "user", "content": "Write a short poem on OpenTelemetry."}], +) + +# The library captures telemetry, including request and response metadata, token usage, and more. +``` + +## Join Us in Shaping the Future of Generative AI Observability + +Community collaboration is key to OpenTelemetry’s success. We invite developers, AI practitioners, and organizations to contribute, share feedback, or participate in discussions. Explore the OpenTelemetry Python Contrib project, contribute code, or help shape observability for AI as it continues to evolve. More information can be found at the [Generative AI Observability project page](https://github.com/open-telemetry/community/blob/main/projects/gen-ai.md). From 5c9a04bedb53dac0ad995d93050935ca54c5d165 Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Sat, 9 Nov 2024 09:32:37 +0900 Subject: [PATCH 02/32] Add screenshots --- .../aspire_dashboard_content_capture.png | Bin 0 -> 138879 bytes .../aspire_dashboard_trace.png | Bin 0 -> 467690 bytes .../en/blog/2024/otel-generative-ai/index.md | 31 ++++++++++++++++-- .../2024/otel-generative-ai/jaeger_trace.png | Bin 0 -> 527038 bytes 4 files changed, 28 insertions(+), 3 deletions(-) create mode 100644 content/en/blog/2024/otel-generative-ai/aspire_dashboard_content_capture.png create mode 100644 content/en/blog/2024/otel-generative-ai/aspire_dashboard_trace.png create mode 100644 content/en/blog/2024/otel-generative-ai/jaeger_trace.png diff --git a/content/en/blog/2024/otel-generative-ai/aspire_dashboard_content_capture.png b/content/en/blog/2024/otel-generative-ai/aspire_dashboard_content_capture.png new file mode 100644 index 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Robbins](https://github.com/drewby) (Microsoft), [Luidmila Molkova](https://github.com/lmolkova) (Microsoft) @@ -42,11 +42,18 @@ As generative AI applications grow, additional instrumentation libraries for oth ### Example Usage -Here’s an example of using the OpenTelemetry Python library to monitor a generative AI application with the OpenAI client: +Here’s an example of using the OpenTelemetry Python library to monitor a generative AI application with the OpenAI client. +Make sure you first install the library: + +```bash +pip install opentelemetry-instrumentation-openai-v2 +``` + +Then include the following code in your python application. ```python from openai import OpenAI -from opentelemetry.instrumentation.openai import OpenAIInstrumentor +from opentelemetry.instrumentation.openai_v2 import OpenAIInstrumentor OpenAIInstrumentor().instrument() @@ -59,6 +66,24 @@ response = client.chat.completions.create( # The library captures telemetry, including request and response metadata, token usage, and more. ``` +With this simple instrumentation, one can begin capture traces from their generative AI application. Here is an example from the Aspire Dashboard for local debugging. + +![Chat trace in Aspire Dashboard](aspire_dashboard_trace.png) + +Here is a similar trace captured in Jaeger: + +![Chat trace in Jaeger](jaeger_trace.png) + +It's also easy to capture the content history of the chat for debugging and improving your application. Simply set the environment variable `OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT` as follows: + +```bash +export OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=True +``` + +This will turn on content capture which collects OpenTelemetry events containing the payload: + +![Content Capture Aspire Dashboard](aspire_dashboard_content_capture.png) + ## Join Us in Shaping the Future of Generative AI Observability Community collaboration is key to OpenTelemetry’s success. We invite developers, AI practitioners, and organizations to contribute, share feedback, or participate in discussions. Explore the OpenTelemetry Python Contrib project, contribute code, or help shape observability for AI as it continues to evolve. 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zY>TLJ?cSl$PYehM5tWZRbX{k&?d{oOe7uXux_B;zDM#t&${sjO<&LEErbRU|52iVMm{r!6PrgHS67%;fkOimsY zZuh{aZ2L%ttv+Lmsvn_jRJ$t@B>mp+=m=)QGUjk`!A&}q1n)h6V}wAnNKS&MJiKC zt*;s;pH8|8Ij-!U2(A^3NCsv3<3itj7bqCWyjAPIthygk3nI8wuGKZzSkxK4vHX(> zqLuq1f?FdJ!ut@9I-DxVLml=J_&^+pVWhA;{|Ig_*Sk(~+OxH*Oe$6`R&LssNba?3 zi56%GCivA8c1|3zY(9|b<^#$2Ibeh-02q~__&D+qYPlQbX)- Molkova](https://github.com/lmolkova) (Microsoft) --- -As organizations increasingly adopt Large Language Models (LLMs) and other generative AI technologies, ensuring reliable performance, efficiency, and safety is essential. Enhanced observability—tracking AI operations, behaviors, and outcomes—helps meet these goals. OpenTelemetry is being enhanced to support these needs specifically for generative AI. - -Two primary assets are in development to make this possible: **Semantic Conventions** and an **Instrumentation Library**. - -**Semantic Conventions** establish standardized guidelines for how telemetry data is structured and collected across platforms, defining inputs, outputs, and operational details. For generative AI, these conventions streamline monitoring, troubleshooting, and optimizing AI models by standardizing attributes such as model parameters, response metadata, and token usage. This consistency supports better observability across tools, environments, and APIs, helping organizations track performance, cost, and safety with ease. - -The **Instrumentation Library** is being developed within the OpenTelemetry Python Contrib project to automate telemetry collection for generative AI applications. The first release is a Python library, given Python’s widespread use in AI development. Designed to integrate seamlessly with OpenAI’s API, this library captures spans, metrics, and events, gathering essential data like model inputs, response metadata, and token usage in a structured format. +As organizations increasingly adopt Large Language Models (LLMs) and other +generative AI technologies, ensuring reliable performance, efficiency, and +safety is essential. Enhanced observability—tracking AI operations, behaviors, +and outcomes—helps meet these goals. OpenTelemetry is being enhanced to support +these needs specifically for generative AI. + +Two primary assets are in development to make this possible: **Semantic +Conventions** and an **Instrumentation Library**. + +**Semantic Conventions** establish standardized guidelines for how telemetry +data is structured and collected across platforms, defining inputs, outputs, and +operational details. For generative AI, these conventions streamline monitoring, +troubleshooting, and optimizing AI models by standardizing attributes such as +model parameters, response metadata, and token usage. This consistency supports +better observability across tools, environments, and APIs, helping organizations +track performance, cost, and safety with ease. + +The **Instrumentation Library** is being developed within the OpenTelemetry +Python Contrib project to automate telemetry collection for generative AI +applications. The first release is a Python library, given Python’s widespread +use in AI development. Designed to integrate seamlessly with OpenAI’s API, this +library captures spans, metrics, and events, gathering essential data like model +inputs, response metadata, and token usage in a structured format. ## Key Signals for Generative AI -The [Semantic Conventions for Generative AI](https://github.com/open-telemetry/semantic-conventions/tree/main/docs/gen-ai) focus on capturing insights into AI model behavior through three primary signals: Spans, Metrics, and Events. +The +[Semantic Conventions for Generative AI](https://github.com/open-telemetry/semantic-conventions/tree/main/docs/gen-ai) +focus on capturing insights into AI model behavior through three primary +signals: Spans, Metrics, and Events. **Spans: Tracing Model Interactions** -Spans track each model interaction’s lifecycle, covering input parameters (e.g., temperature, top_p) and response details like token count or errors. They provide visibility into each request, aiding in identifying bottlenecks and analyzing the impact of settings on model output. +Spans track each model interaction’s lifecycle, covering input parameters (e.g., +temperature, top_p) and response details like token count or errors. They +provide visibility into each request, aiding in identifying bottlenecks and +analyzing the impact of settings on model output. **Metrics: Monitoring Usage and Performance** -Metrics aggregate high-level indicators like request volume, latency, and resource use, essential for managing costs and performance. This data is particularly critical for API-dependent AI applications with rate limits and cost considerations. +Metrics aggregate high-level indicators like request volume, latency, and +resource use, essential for managing costs and performance. This data is +particularly critical for API-dependent AI applications with rate limits and +cost considerations. **Events: Capturing Detailed Interactions** -Events log detailed moments during model execution, such as user prompts and model responses, providing a granular view of model interactions. These insights are invaluable for debugging and optimizing AI applications where unexpected behaviors may arise. +Events log detailed moments during model execution, such as user prompts and +model responses, providing a granular view of model interactions. These insights +are invaluable for debugging and optimizing AI applications where unexpected +behaviors may arise. -Together, these signals provide a comprehensive monitoring framework, enabling better cost management, performance tuning, and request tracing. +Together, these signals provide a comprehensive monitoring framework, enabling +better cost management, performance tuning, and request tracing. **Extending Observability with Vendor-Specific Attributes** -The Semantic Conventions also define vendor-specific attributes for platforms like OpenAI and Azure Inference API, ensuring telemetry captures both general and provider-specific details. This added flexibility supports multi-platform monitoring and in-depth insights. +The Semantic Conventions also define vendor-specific attributes for platforms +like OpenAI and Azure Inference API, ensuring telemetry captures both general +and provider-specific details. This added flexibility supports multi-platform +monitoring and in-depth insights. ## Building the Python Instrumentation Library for OpenAI -This Python-based library for OpenTelemetry captures key telemetry signals for OpenAI models, providing developers with an out-of-the-box observability solution tailored to AI workloads. The library, [hosted within the OpenTelemetry Contrib project](https://github.com/open-telemetry/opentelemetry-python-contrib/tree/main/instrumentation-genai/opentelemetry-instrumentation-openai-v2), automatically collects telemetry from OpenAI model interactions, including request and response metadata, token usage, and operational metrics. +This Python-based library for OpenTelemetry captures key telemetry signals for +OpenAI models, providing developers with an out-of-the-box observability +solution tailored to AI workloads. The library, +[hosted within the OpenTelemetry Python Contrib repo](https://github.com/open-telemetry/opentelemetry-python-contrib/tree/opentelemetry-instrumentation-openai-v2%3D%3D2.0b0/instrumentation-genai/opentelemetry-instrumentation-openai-v2), +automatically collects telemetry from OpenAI model interactions, including +request and response metadata and token usage. -As generative AI applications grow, additional instrumentation libraries for other languages will follow, extending OpenTelemetry support across more tools and environments. The current library’s focus on OpenAI highlights its popularity and demand within AI development, making it a valuable initial implementation. +As generative AI applications grow, additional instrumentation libraries for +other languages will follow, extending OpenTelemetry support across more tools +and environments. The current library’s focus on OpenAI highlights its +popularity and demand within AI development, making it a valuable initial +implementation. ### Example Usage -Here’s an example of using the OpenTelemetry Python library to monitor a generative AI application with the OpenAI client. -Make sure you first install the library: +Here’s an example of using the OpenTelemetry Python library to monitor a +generative AI application with the OpenAI client. Make sure you first install +the library: ```bash pip install opentelemetry-instrumentation-openai-v2 ``` -Then include the following code in your python application. +Then include the following code in your Python application. ```python from openai import OpenAI @@ -68,22 +110,30 @@ response = client.chat.completions.create( With this simple instrumentation, one can begin capture traces from their generative AI application. Here is an example from the [Aspire Dashboard](https://learn.microsoft.com/dotnet/aspire/fundamentals/dashboard/standalone?tabs=bash) for local debugging. -![Chat trace in Aspire Dashboard](aspire_dashboard_trace.png) +![Chat trace in Aspire Dashboard](aspire-dashboard-trace.png) Here is a similar trace captured in [Jaeger](https://www.jaegertracing.io/docs/next-release-v2/getting-started/#running): -![Chat trace in Jaeger](jaeger_trace.png) +![Chat trace in Jaeger](jaeger-trace.png) -It's also easy to capture the content history of the chat for debugging and improving your application. Simply set the environment variable `OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT` as follows: +It's also easy to capture the content history of the chat for debugging and +improving your application. Simply set the environment variable +`OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT` as follows: ```bash export OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=True ``` -This will turn on content capture which collects OpenTelemetry events containing the payload: +This will turn on content capture which collects OpenTelemetry events containing +the payload: -![Content Capture Aspire Dashboard](aspire_dashboard_content_capture.png) +![Content Capture Aspire Dashboard](aspire-dashboard-content-capture.png) ## Join Us in Shaping the Future of Generative AI Observability -Community collaboration is key to OpenTelemetry’s success. We invite developers, AI practitioners, and organizations to contribute, share feedback, or participate in discussions. Explore the OpenTelemetry Python Contrib project, contribute code, or help shape observability for AI as it continues to evolve. More information can be found at the [Generative AI Observability project page](https://github.com/open-telemetry/community/blob/main/projects/gen-ai.md). +Community collaboration is key to OpenTelemetry’s success. We invite developers, +AI practitioners, and organizations to contribute, share feedback, or +participate in discussions. 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version --- content/en/blog/2024/otel-generative-ai/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index 77e885ec205d..d63cb07f33f5 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -34,7 +34,7 @@ inputs, response metadata, and token usage in a structured format. ## Key Signals for Generative AI The -[Semantic Conventions for Generative AI](https://github.com/open-telemetry/semantic-conventions/tree/main/docs/gen-ai) +[Semantic Conventions for Generative AI](https://github.com/open-telemetry/semantic-conventions/tree/v1.28.0/docs/gen-ai) focus on capturing insights into AI model behavior through three primary signals: Spans, Metrics, and Events. From 8dd9a1198fcd839396d2840fe32e28cb6003e177 Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Sat, 9 Nov 2024 02:13:42 +0000 Subject: [PATCH 11/32] Update linting/spelling error --- content/en/blog/2024/otel-generative-ai/index.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index d63cb07f33f5..8f02c0055bd9 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -60,7 +60,6 @@ Together, these signals provide a comprehensive monitoring framework, enabling better cost management, performance tuning, and request tracing. **Extending Observability with Vendor-Specific Attributes** - The Semantic Conventions also define vendor-specific attributes for platforms like OpenAI and Azure Inference API, ensuring telemetry captures both general and provider-specific details. This added flexibility supports multi-platform @@ -71,7 +70,7 @@ monitoring and in-depth insights. This Python-based library for OpenTelemetry captures key telemetry signals for OpenAI models, providing developers with an out-of-the-box observability solution tailored to AI workloads. The library, -[hosted within the OpenTelemetry Python Contrib repo](https://github.com/open-telemetry/opentelemetry-python-contrib/tree/opentelemetry-instrumentation-openai-v2%3D%3D2.0b0/instrumentation-genai/opentelemetry-instrumentation-openai-v2), +[hosted within the OpenTelemetry Python Contrib repository](https://github.com/open-telemetry/opentelemetry-python-contrib/tree/opentelemetry-instrumentation-openai-v2%3D%3D2.0b0/instrumentation-genai/opentelemetry-instrumentation-openai-v2), automatically collects telemetry from OpenAI model interactions, including request and response metadata and token usage. From 4159b7dacdec7282c87cede9b028e0714023328d Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Sat, 9 Nov 2024 02:18:56 +0000 Subject: [PATCH 12/32] Fix format error --- content/en/blog/2024/otel-generative-ai/index.md | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index 8f02c0055bd9..731e2aea3e74 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -59,10 +59,10 @@ behaviors may arise. Together, these signals provide a comprehensive monitoring framework, enabling better cost management, performance tuning, and request tracing. -**Extending Observability with Vendor-Specific Attributes** -The Semantic Conventions also define vendor-specific attributes for platforms -like OpenAI and Azure Inference API, ensuring telemetry captures both general -and provider-specific details. This added flexibility supports multi-platform +**Extending Observability with Vendor-Specific Attributes** The Semantic +Conventions also define vendor-specific attributes for platforms like OpenAI and +Azure Inference API, ensuring telemetry captures both general and +provider-specific details. This added flexibility supports multi-platform monitoring and in-depth insights. ## Building the Python Instrumentation Library for OpenAI @@ -107,11 +107,15 @@ response = client.chat.completions.create( # The library captures telemetry, including request and response metadata, token usage, and more. ``` -With this simple instrumentation, one can begin capture traces from their generative AI application. Here is an example from the [Aspire Dashboard](https://learn.microsoft.com/dotnet/aspire/fundamentals/dashboard/standalone?tabs=bash) for local debugging. +With this simple instrumentation, one can begin capture traces from their +generative AI application. Here is an example from the +[Aspire Dashboard](https://learn.microsoft.com/dotnet/aspire/fundamentals/dashboard/standalone?tabs=bash) +for local debugging. ![Chat trace in Aspire Dashboard](aspire-dashboard-trace.png) -Here is a similar trace captured in [Jaeger](https://www.jaegertracing.io/docs/next-release-v2/getting-started/#running): +Here is a similar trace captured in +[Jaeger](https://www.jaegertracing.io/docs/next-release-v2/getting-started/#running): ![Chat trace in Jaeger](jaeger-trace.png) From 480235bd46f9d151abbb29475c7880bdb24d0c10 Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Sat, 9 Nov 2024 12:12:16 +0900 Subject: [PATCH 13/32] Updates to library intro and metric section --- content/en/blog/2024/otel-generative-ai/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index 731e2aea3e74..feca8ad12e4a 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -28,7 +28,7 @@ The **Instrumentation Library** is being developed within the OpenTelemetry Python Contrib project to automate telemetry collection for generative AI applications. The first release is a Python library, given Python’s widespread use in AI development. Designed to integrate seamlessly with OpenAI’s API, this -library captures spans, metrics, and events, gathering essential data like model +library captures spans and events, gathering essential data like model inputs, response metadata, and token usage in a structured format. ## Key Signals for Generative AI @@ -46,7 +46,7 @@ analyzing the impact of settings on model output. **Metrics: Monitoring Usage and Performance** Metrics aggregate high-level indicators like request volume, latency, and -resource use, essential for managing costs and performance. This data is +token counts, essential for managing costs and performance. This data is particularly critical for API-dependent AI applications with rate limits and cost considerations. From aef7577f7a0ef72d15873451ffe2a75b41051099 Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Sun, 10 Nov 2024 22:59:13 +0000 Subject: [PATCH 14/32] Update introduction --- content/en/blog/2024/otel-generative-ai/index.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index feca8ad12e4a..f78a2121a322 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -7,11 +7,12 @@ author: >- Molkova](https://github.com/lmolkova) (Microsoft) --- -As organizations increasingly adopt Large Language Models (LLMs) and other -generative AI technologies, ensuring reliable performance, efficiency, and -safety is essential. Enhanced observability—tracking AI operations, behaviors, -and outcomes—helps meet these goals. OpenTelemetry is being enhanced to support -these needs specifically for generative AI. +As organizations increasingly adopt Large Language Models (LLMs) and other +generative AI technologies, ensuring reliable performance, efficiency, and +safety is essential to meet user expectations, optimize resource costs, and +safeguard against unintended outputs. Effective observability for AI +operations, behaviors, and outcomes can help meet these goals. +OpenTelemetry is being enhanced to support these needs specifically for generative AI. Two primary assets are in development to make this possible: **Semantic Conventions** and an **Instrumentation Library**. From 16877ada86592b9c4077c1b37ffa6ad62dfef001 Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Sun, 10 Nov 2024 22:59:59 +0000 Subject: [PATCH 15/32] Use headers instead of bold --- .../en/blog/2024/otel-generative-ai/index.md | 24 +++++++++++-------- 1 file changed, 14 insertions(+), 10 deletions(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index f78a2121a322..323a5531c2e4 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -39,19 +39,22 @@ The focus on capturing insights into AI model behavior through three primary signals: Spans, Metrics, and Events. -**Spans: Tracing Model Interactions** -Spans track each model interaction’s lifecycle, covering input parameters (e.g., -temperature, top_p) and response details like token count or errors. They -provide visibility into each request, aiding in identifying bottlenecks and +### Spans: Tracing Model Interactions + +Spans track each model interaction’s lifecycle, covering input parameters (for +example, temperature, top_p) and response details like token count or errors. +They provide visibility into each request, aiding in identifying bottlenecks and analyzing the impact of settings on model output. +cd +### Metrics: Monitoring Usage and Performance -**Metrics: Monitoring Usage and Performance** Metrics aggregate high-level indicators like request volume, latency, and token counts, essential for managing costs and performance. This data is particularly critical for API-dependent AI applications with rate limits and cost considerations. -**Events: Capturing Detailed Interactions** +### Events: Capturing Detailed Interactions + Events log detailed moments during model execution, such as user prompts and model responses, providing a granular view of model interactions. These insights are invaluable for debugging and optimizing AI applications where unexpected @@ -60,10 +63,11 @@ behaviors may arise. Together, these signals provide a comprehensive monitoring framework, enabling better cost management, performance tuning, and request tracing. -**Extending Observability with Vendor-Specific Attributes** The Semantic -Conventions also define vendor-specific attributes for platforms like OpenAI and -Azure Inference API, ensuring telemetry captures both general and -provider-specific details. This added flexibility supports multi-platform +### Extending Observability with Vendor-Specific Attributes + +The Semantic Conventions also define vendor-specific attributes for platforms +like OpenAI and Azure Inference API, ensuring telemetry captures both general +and provider-specific details. This added flexibility supports multi-platform monitoring and in-depth insights. ## Building the Python Instrumentation Library for OpenAI From c7e3ca97175ec60890ac67dc8fd8939b9614a2e2 Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Sun, 10 Nov 2024 23:00:57 +0000 Subject: [PATCH 16/32] Fix formatting --- content/en/blog/2024/otel-generative-ai/index.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index 323a5531c2e4..cee8e2d44159 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -7,12 +7,12 @@ author: >- Molkova](https://github.com/lmolkova) (Microsoft) --- -As organizations increasingly adopt Large Language Models (LLMs) and other -generative AI technologies, ensuring reliable performance, efficiency, and -safety is essential to meet user expectations, optimize resource costs, and -safeguard against unintended outputs. Effective observability for AI -operations, behaviors, and outcomes can help meet these goals. -OpenTelemetry is being enhanced to support these needs specifically for generative AI. +As organizations increasingly adopt Large Language Models (LLMs) and other +generative AI technologies, ensuring reliable performance, efficiency, and +safety is essential to meet user expectations, optimize resource costs, and +safeguard against unintended outputs. Effective observability for AI operations, +behaviors, and outcomes can help meet these goals. OpenTelemetry is being +enhanced to support these needs specifically for generative AI. Two primary assets are in development to make this possible: **Semantic Conventions** and an **Instrumentation Library**. From 9f248fe9026736a1d90b02b8111f4eb24cd74821 Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Mon, 11 Nov 2024 18:03:33 +0900 Subject: [PATCH 17/32] Link to docs page Co-authored-by: Severin Neumann --- content/en/blog/2024/otel-generative-ai/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index cee8e2d44159..c6b67897a3b5 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -17,7 +17,7 @@ enhanced to support these needs specifically for generative AI. Two primary assets are in development to make this possible: **Semantic Conventions** and an **Instrumentation Library**. -**Semantic Conventions** establish standardized guidelines for how telemetry +[**Semantic Conventions** ](https://opentelemetry.io/docs/concepts/semantic-conventions/) establish standardized guidelines for how telemetry data is structured and collected across platforms, defining inputs, outputs, and operational details. For generative AI, these conventions streamline monitoring, troubleshooting, and optimizing AI models by standardizing attributes such as From 9813dc821de775b642fbfcbef52b2a6651aaa352 Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Mon, 11 Nov 2024 18:04:24 +0900 Subject: [PATCH 18/32] Add links to spec and python projects Co-authored-by: Severin Neumann --- content/en/blog/2024/otel-generative-ai/index.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index c6b67897a3b5..8db93d6c41b5 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -25,8 +25,7 @@ model parameters, response metadata, and token usage. This consistency supports better observability across tools, environments, and APIs, helping organizations track performance, cost, and safety with ease. -The **Instrumentation Library** is being developed within the OpenTelemetry -Python Contrib project to automate telemetry collection for generative AI +The [**Instrumentation Library**](https://opentelemetry.io/docs/specs/otel/overview/#instrumentation-libraries) is being developed within the [OpenTelemetry Python Contrib](https://github.com/open-telemetry/opentelemetry-python-contrib) project to automate telemetry collection for generative AI applications. The first release is a Python library, given Python’s widespread use in AI development. Designed to integrate seamlessly with OpenAI’s API, this library captures spans and events, gathering essential data like model From c183cb67679a091b24d5d7ad7821b3576f9f68fa Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Mon, 11 Nov 2024 18:07:00 +0900 Subject: [PATCH 19/32] Colon instead of period Co-authored-by: Severin Neumann --- content/en/blog/2024/otel-generative-ai/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index 8db93d6c41b5..d307f0f9ae2a 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -94,7 +94,7 @@ the library: pip install opentelemetry-instrumentation-openai-v2 ``` -Then include the following code in your Python application. +Then include the following code in your Python application: ```python from openai import OpenAI From 17ec7549e294425bcd8f60432857b3e00593ad50 Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Mon, 11 Nov 2024 18:11:40 +0900 Subject: [PATCH 20/32] Add issue and sig --- content/en/blog/2024/otel-generative-ai/index.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index d307f0f9ae2a..fcc7823012bd 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -5,6 +5,8 @@ date: 2024-11-09 author: >- [Drew Robbins](https://github.com/drewby) (Microsoft), [Liudmila Molkova](https://github.com/lmolkova) (Microsoft) +issue: [#5581](https://github.com/open-telemetry/opentelemetry.io/issues/5581) +sig: SIG GenAI Observability --- As organizations increasingly adopt Large Language Models (LLMs) and other From d421b70edf6e536f6efd5b9ee34c661aeeb295c5 Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Mon, 11 Nov 2024 18:14:58 +0900 Subject: [PATCH 21/32] Move text to flow better in outline --- content/en/blog/2024/otel-generative-ai/index.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index fcc7823012bd..554d36e5bd60 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -40,13 +40,16 @@ The focus on capturing insights into AI model behavior through three primary signals: Spans, Metrics, and Events. +Together, these signals provide a comprehensive monitoring framework, enabling +better cost management, performance tuning, and request tracing. + ### Spans: Tracing Model Interactions Spans track each model interaction’s lifecycle, covering input parameters (for example, temperature, top_p) and response details like token count or errors. They provide visibility into each request, aiding in identifying bottlenecks and analyzing the impact of settings on model output. -cd + ### Metrics: Monitoring Usage and Performance Metrics aggregate high-level indicators like request volume, latency, and @@ -61,9 +64,6 @@ model responses, providing a granular view of model interactions. These insights are invaluable for debugging and optimizing AI applications where unexpected behaviors may arise. -Together, these signals provide a comprehensive monitoring framework, enabling -better cost management, performance tuning, and request tracing. - ### Extending Observability with Vendor-Specific Attributes The Semantic Conventions also define vendor-specific attributes for platforms From 915918b5a40e1974cf4e811e4c8f7c4b5cc2ab74 Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Mon, 11 Nov 2024 09:19:37 +0000 Subject: [PATCH 22/32] Clarify library focus --- content/en/blog/2024/otel-generative-ai/index.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index 554d36e5bd60..8900da10d69c 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -19,7 +19,7 @@ enhanced to support these needs specifically for generative AI. Two primary assets are in development to make this possible: **Semantic Conventions** and an **Instrumentation Library**. -[**Semantic Conventions** ](https://opentelemetry.io/docs/concepts/semantic-conventions/) establish standardized guidelines for how telemetry +[**Semantic Conventions**](https://opentelemetry.io/docs/concepts/semantic-conventions/) establish standardized guidelines for how telemetry data is structured and collected across platforms, defining inputs, outputs, and operational details. For generative AI, these conventions streamline monitoring, troubleshooting, and optimizing AI models by standardizing attributes such as @@ -28,8 +28,8 @@ better observability across tools, environments, and APIs, helping organizations track performance, cost, and safety with ease. The [**Instrumentation Library**](https://opentelemetry.io/docs/specs/otel/overview/#instrumentation-libraries) is being developed within the [OpenTelemetry Python Contrib](https://github.com/open-telemetry/opentelemetry-python-contrib) project to automate telemetry collection for generative AI -applications. The first release is a Python library, given Python’s widespread -use in AI development. Designed to integrate seamlessly with OpenAI’s API, this +applications. The first release is a Python library for instrumenting OpenAI client calls, given Python's widespread +use in AI development and the popularity of OpenAI. Designed to integrate seamlessly with OpenAI's API, this library captures spans and events, gathering essential data like model inputs, response metadata, and token usage in a structured format. From 38b96199c737b15871f95cfdd2feb4d0f8284889 Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Mon, 11 Nov 2024 09:31:09 +0000 Subject: [PATCH 23/32] Add comment about using Events --- .../en/blog/2024/otel-generative-ai/index.md | 53 ++++++++++++------- 1 file changed, 34 insertions(+), 19 deletions(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index 8900da10d69c..9235dfbab3b4 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -19,31 +19,39 @@ enhanced to support these needs specifically for generative AI. Two primary assets are in development to make this possible: **Semantic Conventions** and an **Instrumentation Library**. -[**Semantic Conventions**](https://opentelemetry.io/docs/concepts/semantic-conventions/) establish standardized guidelines for how telemetry -data is structured and collected across platforms, defining inputs, outputs, and -operational details. For generative AI, these conventions streamline monitoring, -troubleshooting, and optimizing AI models by standardizing attributes such as -model parameters, response metadata, and token usage. This consistency supports -better observability across tools, environments, and APIs, helping organizations -track performance, cost, and safety with ease. - -The [**Instrumentation Library**](https://opentelemetry.io/docs/specs/otel/overview/#instrumentation-libraries) is being developed within the [OpenTelemetry Python Contrib](https://github.com/open-telemetry/opentelemetry-python-contrib) project to automate telemetry collection for generative AI -applications. The first release is a Python library for instrumenting OpenAI client calls, given Python's widespread -use in AI development and the popularity of OpenAI. Designed to integrate seamlessly with OpenAI's API, this -library captures spans and events, gathering essential data like model -inputs, response metadata, and token usage in a structured format. +[**Semantic Conventions**](https://opentelemetry.io/docs/concepts/semantic-conventions/) +establish standardized guidelines for how telemetry data is structured and +collected across platforms, defining inputs, outputs, and operational details. +For generative AI, these conventions streamline monitoring, troubleshooting, and +optimizing AI models by standardizing attributes such as model parameters, +response metadata, and token usage. This consistency supports better +observability across tools, environments, and APIs, helping organizations track +performance, cost, and safety with ease. + +The +[**Instrumentation Library**](https://opentelemetry.io/docs/specs/otel/overview/#instrumentation-libraries) +is being developed within the +[OpenTelemetry Python Contrib](https://github.com/open-telemetry/opentelemetry-python-contrib) +project to automate telemetry collection for generative AI applications. The +first release is a Python library for instrumenting OpenAI client calls, given +Python's widespread use in AI development and the popularity of OpenAI. Designed +to integrate seamlessly with OpenAI's API, this library captures spans and +events, gathering essential data like model inputs, response metadata, and token +usage in a structured format. ## Key Signals for Generative AI The [Semantic Conventions for Generative AI](https://github.com/open-telemetry/semantic-conventions/tree/v1.28.0/docs/gen-ai) focus on capturing insights into AI model behavior through three primary -signals: Spans, Metrics, and Events. +signals: [Traces](https://opentelemetry.io/docs/concepts/signals/traces/), +[Metrics](https://opentelemetry.io/docs/concepts/signals/metrics/), and +[Events](https://opentelemetry.io/docs/specs/otel/logs/event-api/). Together, these signals provide a comprehensive monitoring framework, enabling better cost management, performance tuning, and request tracing. -### Spans: Tracing Model Interactions +### Traces: Tracing Model Interactions Spans track each model interaction’s lifecycle, covering input parameters (for example, temperature, top_p) and response details like token count or errors. @@ -52,10 +60,10 @@ analyzing the impact of settings on model output. ### Metrics: Monitoring Usage and Performance -Metrics aggregate high-level indicators like request volume, latency, and -token counts, essential for managing costs and performance. This data is -particularly critical for API-dependent AI applications with rate limits and -cost considerations. +Metrics aggregate high-level indicators like request volume, latency, and token +counts, essential for managing costs and performance. This data is particularly +critical for API-dependent AI applications with rate limits and cost +considerations. ### Events: Capturing Detailed Interactions @@ -64,6 +72,13 @@ model responses, providing a granular view of model interactions. These insights are invaluable for debugging and optimizing AI applications where unexpected behaviors may arise. +> Note that we decided to use the newer Events API +> (https://opentelemetry.io/docs/specs/otel/logs/event-api/) specification in +> the Semantic Conventions for Generative AI. The events API allows for us to +> define specific +> [semantic conventions](https://opentelemetry.io/docs/specs/semconv/general/events/) +> for the user prompts and model responses that we capture. + ### Extending Observability with Vendor-Specific Attributes The Semantic Conventions also define vendor-specific attributes for platforms From 466513783eb00e440f79b89af52f24d94449b00f Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Mon, 11 Nov 2024 09:32:11 +0000 Subject: [PATCH 24/32] Change Spans to Traces --- content/en/blog/2024/otel-generative-ai/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index 9235dfbab3b4..202c2e0c77de 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -53,7 +53,7 @@ better cost management, performance tuning, and request tracing. ### Traces: Tracing Model Interactions -Spans track each model interaction’s lifecycle, covering input parameters (for +Traces track each model interaction’s lifecycle, covering input parameters (for example, temperature, top_p) and response details like token count or errors. They provide visibility into each request, aiding in identifying bottlenecks and analyzing the impact of settings on model output. From 917e93160fac71d0d2b6020aaf7111adfee38acc Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Tue, 12 Nov 2024 14:50:17 +0900 Subject: [PATCH 25/32] Specifics about the first Instrumentation Library Co-authored-by: Adrian Cole <64215+codefromthecrypt@users.noreply.github.com> --- content/en/blog/2024/otel-generative-ai/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index 202c2e0c77de..34a4a3a27cc9 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -17,7 +17,7 @@ behaviors, and outcomes can help meet these goals. OpenTelemetry is being enhanced to support these needs specifically for generative AI. Two primary assets are in development to make this possible: **Semantic -Conventions** and an **Instrumentation Library**. +Conventions** and an **Instrumentation Library**. The first instrumentation library targets OpenAI in Python. [**Semantic Conventions**](https://opentelemetry.io/docs/concepts/semantic-conventions/) establish standardized guidelines for how telemetry data is structured and From 35dca249201a122441ff10a317ce31211b0ecdbd Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Tue, 12 Nov 2024 05:59:59 +0000 Subject: [PATCH 26/32] Use alert shortcode --- content/en/blog/2024/otel-generative-ai/index.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index 34a4a3a27cc9..d27822615014 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -72,12 +72,12 @@ model responses, providing a granular view of model interactions. These insights are invaluable for debugging and optimizing AI applications where unexpected behaviors may arise. -> Note that we decided to use the newer Events API -> (https://opentelemetry.io/docs/specs/otel/logs/event-api/) specification in -> the Semantic Conventions for Generative AI. The events API allows for us to -> define specific -> [semantic conventions](https://opentelemetry.io/docs/specs/semconv/general/events/) -> for the user prompts and model responses that we capture. +{{% alert title="Note" color="info" %}} Note that we decided to use the +newer Events API (https://opentelemetry.io/docs/specs/otel/logs/event-api/) +specification in the Semantic Conventions for Generative AI. The events API +allows for us to define specific +[semantic conventions](https://opentelemetry.io/docs/specs/semconv/general/events/) +for the user prompts and model responses that we capture. {{% /alert %}} ### Extending Observability with Vendor-Specific Attributes From 78607e10fd5aa84d3b77d49c302e7e3a9bc68fd5 Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Wed, 13 Nov 2024 09:17:09 +0900 Subject: [PATCH 27/32] Add link to instrumentation library Co-authored-by: Guangya Liu --- content/en/blog/2024/otel-generative-ai/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index d27822615014..2eef87b85174 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -31,7 +31,7 @@ performance, cost, and safety with ease. The [**Instrumentation Library**](https://opentelemetry.io/docs/specs/otel/overview/#instrumentation-libraries) is being developed within the -[OpenTelemetry Python Contrib](https://github.com/open-telemetry/opentelemetry-python-contrib) +[OpenTelemetry Python Contrib](https://github.com/open-telemetry/opentelemetry-python-contrib) under [instrumentation-genai](https://github.com/open-telemetry/opentelemetry-python-contrib/tree/main/instrumentation-genai) project to automate telemetry collection for generative AI applications. The first release is a Python library for instrumenting OpenAI client calls, given Python's widespread use in AI development and the popularity of OpenAI. Designed From 638e3b4efbd40eb4754113c68f3ace2b1600075e Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Thu, 14 Nov 2024 03:57:26 +0000 Subject: [PATCH 28/32] Move words from cspell to article header --- .cspell/en-words.txt | 5 +---- .../en/blog/2024/otel-generative-ai/index.md | 20 +++++++++++++++++++ 2 files changed, 21 insertions(+), 4 deletions(-) diff --git a/.cspell/en-words.txt b/.cspell/en-words.txt index afe2fdef996f..03be93c7594f 100644 --- a/.cspell/en-words.txt +++ b/.cspell/en-words.txt @@ -143,7 +143,4 @@ Villela wordpress WSGI zend -zipkin -Liudmila -Molkova -GENAI \ No newline at end of file +zipkin \ No newline at end of file diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index 2eef87b85174..3a9b41ed49ee 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -7,6 +7,7 @@ author: >- Molkova](https://github.com/lmolkova) (Microsoft) issue: [#5581](https://github.com/open-telemetry/opentelemetry.io/issues/5581) sig: SIG GenAI Observability +cSpell:ignore: genai liudmila molkova --- As organizations increasingly adopt Large Language Models (LLMs) and other @@ -114,6 +115,25 @@ pip install opentelemetry-instrumentation-openai-v2 Then include the following code in your Python application: ```python +from opentelemetry import trace, _logs, _events +from opentelemetry.sdk.trace import TracerProvider +from opentelemetry.sdk._logs import LoggerProvider +from opentelemetry.sdk._events import EventLoggerProvider + +from opentelemetry.sdk.trace.export import BatchSpanProcessor +from opentelemetry.sdk._logs.export import BatchLogRecordProcessor +from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter +from opentelemetry.exporter.otlp.proto.grpc._log_exporter import OTLPLogExporter + +trace.set_tracer_provider(TracerProvider()) +trace.get_tracer_provider().add_span_processor( + BatchSpanProcessor(OTLPSpanExporter()) +) + +_logs.set_logger_provider(LoggerProvider()) +_logs.get_logger_provider().add_log_record_processor(BatchLogRecordProcessor(OTLPLogExporter())) +_events.set_event_logger_provider(EventLoggerProvider()) + from openai import OpenAI from opentelemetry.instrumentation.openai_v2 import OpenAIInstrumentor From 0303e369de897604459c19937076299f4c2afbf3 Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Thu, 14 Nov 2024 04:02:31 +0000 Subject: [PATCH 29/32] Fix lint/link errors --- content/en/blog/2024/otel-generative-ai/index.md | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index 3a9b41ed49ee..6467a0bb187a 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -18,7 +18,8 @@ behaviors, and outcomes can help meet these goals. OpenTelemetry is being enhanced to support these needs specifically for generative AI. Two primary assets are in development to make this possible: **Semantic -Conventions** and an **Instrumentation Library**. The first instrumentation library targets OpenAI in Python. +Conventions** and an **Instrumentation Library**. The first instrumentation +library targets OpenAI in Python. [**Semantic Conventions**](https://opentelemetry.io/docs/concepts/semantic-conventions/) establish standardized guidelines for how telemetry data is structured and @@ -32,7 +33,9 @@ performance, cost, and safety with ease. The [**Instrumentation Library**](https://opentelemetry.io/docs/specs/otel/overview/#instrumentation-libraries) is being developed within the -[OpenTelemetry Python Contrib](https://github.com/open-telemetry/opentelemetry-python-contrib) under [instrumentation-genai](https://github.com/open-telemetry/opentelemetry-python-contrib/tree/main/instrumentation-genai) +[OpenTelemetry Python Contrib](https://github.com/open-telemetry/opentelemetry-python-contrib) +under +[instrumentation-genai](https://github.com/open-telemetry/opentelemetry-python-contrib/tree/main/instrumentation-genai) project to automate telemetry collection for generative AI applications. The first release is a Python library for instrumenting OpenAI client calls, given Python's widespread use in AI development and the popularity of OpenAI. Designed @@ -73,8 +76,8 @@ model responses, providing a granular view of model interactions. These insights are invaluable for debugging and optimizing AI applications where unexpected behaviors may arise. -{{% alert title="Note" color="info" %}} Note that we decided to use the -newer Events API (https://opentelemetry.io/docs/specs/otel/logs/event-api/) +{{% alert title="Note" color="info" %}} Note that we decided to use the newer +[Log events API](https://opentelemetry.io/docs/specs/otel/logs/event-api/) specification in the Semantic Conventions for Generative AI. The events API allows for us to define specific [semantic conventions](https://opentelemetry.io/docs/specs/semconv/general/events/) From a1489fb5a3e4861a4db121e4bb80c3c784d11adc Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Thu, 14 Nov 2024 04:12:11 +0000 Subject: [PATCH 30/32] Update code sample --- .../en/blog/2024/otel-generative-ai/index.md | 31 +++++++++---------- 1 file changed, 15 insertions(+), 16 deletions(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index 6467a0bb187a..24302bc3bb38 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -115,26 +115,25 @@ the library: pip install opentelemetry-instrumentation-openai-v2 ``` +Set the following environment variables, updating the endpoint and protocol as appropriate: + +```bash +OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4318 +OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf +OTEL_SERVICE_NAME=python-opentelemetry-openai + +# Set to false or remove to disable log events +OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=true +OTEL_PYTHON_LOGGING_AUTO_INSTRUMENTATION_ENABLED=true +OTEL_LOGS_EXPORTER=otlp_proto_http +``` + Then include the following code in your Python application: ```python -from opentelemetry import trace, _logs, _events -from opentelemetry.sdk.trace import TracerProvider -from opentelemetry.sdk._logs import LoggerProvider +# NOTE: OpenTelemetry Python Log Events APIs is in beta +from opentelemetry import _events from opentelemetry.sdk._events import EventLoggerProvider - -from opentelemetry.sdk.trace.export import BatchSpanProcessor -from opentelemetry.sdk._logs.export import BatchLogRecordProcessor -from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter -from opentelemetry.exporter.otlp.proto.grpc._log_exporter import OTLPLogExporter - -trace.set_tracer_provider(TracerProvider()) -trace.get_tracer_provider().add_span_processor( - BatchSpanProcessor(OTLPSpanExporter()) -) - -_logs.set_logger_provider(LoggerProvider()) -_logs.get_logger_provider().add_log_record_processor(BatchLogRecordProcessor(OTLPLogExporter())) _events.set_event_logger_provider(EventLoggerProvider()) from openai import OpenAI From 730e1c14bcf36f45e9504c5089974b4cac61bf8e Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Thu, 14 Nov 2024 04:12:34 +0000 Subject: [PATCH 31/32] fix format --- content/en/blog/2024/otel-generative-ai/index.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index 24302bc3bb38..6ffe789b55b6 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -115,7 +115,8 @@ the library: pip install opentelemetry-instrumentation-openai-v2 ``` -Set the following environment variables, updating the endpoint and protocol as appropriate: +Set the following environment variables, updating the endpoint and protocol as +appropriate: ```bash OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4318 From 64cd559ef145dd0503b8c4e986b523fd9cc803f3 Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Thu, 14 Nov 2024 05:03:30 +0000 Subject: [PATCH 32/32] Update code sample --- .../en/blog/2024/otel-generative-ai/index.md | 28 +++++++++++++++---- 1 file changed, 23 insertions(+), 5 deletions(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index 6ffe789b55b6..ab81fb764a46 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -122,29 +122,47 @@ appropriate: OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4318 OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf OTEL_SERVICE_NAME=python-opentelemetry-openai +OPENAI_API_KEY= # Set to false or remove to disable log events OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=true -OTEL_PYTHON_LOGGING_AUTO_INSTRUMENTATION_ENABLED=true -OTEL_LOGS_EXPORTER=otlp_proto_http ``` Then include the following code in your Python application: ```python + # NOTE: OpenTelemetry Python Log Events APIs is in beta -from opentelemetry import _events +from opentelemetry.sdk.trace import TracerProvider +from opentelemetry.sdk._logs import LoggerProvider from opentelemetry.sdk._events import EventLoggerProvider -_events.set_event_logger_provider(EventLoggerProvider()) +from opentelemetry.trace import set_tracer_provider +from opentelemetry._logs import set_logger_provider +from opentelemetry._events import set_event_logger_provider +from opentelemetry.sdk.trace.export import SimpleSpanProcessor +from opentelemetry.sdk._logs.export import SimpleLogRecordProcessor +from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter +from opentelemetry.exporter.otlp.proto.grpc._log_exporter import OTLPLogExporter from openai import OpenAI from opentelemetry.instrumentation.openai_v2 import OpenAIInstrumentor +provider = TracerProvider() +provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter())) +set_tracer_provider(provider) + +provider = LoggerProvider() +provider.add_log_record_processor(SimpleLogRecordProcessor(OTLPLogExporter())) +set_logger_provider(provider) + +event_provider = EventLoggerProvider(provider) +set_event_logger_provider(event_provider) + OpenAIInstrumentor().instrument() client = OpenAI() response = client.chat.completions.create( - model="gpt-4-mini", + model="gpt-4o", messages=[{"role": "user", "content": "Write a short poem on OpenTelemetry."}], )

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link for Python repo --- content/en/blog/2024/otel-generative-ai/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index 3331fa2d45ef..e63731316567 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -36,7 +36,7 @@ The Semantic Conventions also define vendor-specific attributes for platforms li ## Building the Python Instrumentation Library for OpenAI -This Python-based library for OpenTelemetry captures key telemetry signals for OpenAI models, providing developers with an out-of-the-box observability solution tailored to AI workloads. The library, [hosted within the OpenTelemetry Contrib project](https://github.com/open-telemetry/opentelemetry-python-contrib/tree/main/instrumentation/opentelemetry-instrumentation-openai-v2), automatically collects telemetry from OpenAI model interactions, including request and response metadata, token usage, and operational metrics. +This Python-based library for OpenTelemetry captures key telemetry signals for OpenAI models, providing developers with an out-of-the-box observability solution tailored to AI workloads. The library, [hosted within the OpenTelemetry Contrib project](https://github.com/open-telemetry/opentelemetry-python-contrib/tree/main/instrumentation-genai/opentelemetry-instrumentation-openai-v2), automatically collects telemetry from OpenAI model interactions, including request and response metadata, token usage, and operational metrics. As generative AI applications grow, additional instrumentation libraries for other languages will follow, extending OpenTelemetry support across more tools and environments. The current library’s focus on OpenAI highlights its popularity and demand within AI development, making it a valuable initial implementation. From db189215966d323c895c7193c9ac394152334fb9 Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Sat, 9 Nov 2024 09:56:10 +0900 Subject: [PATCH 04/32] fix spelling error Co-authored-by: Liudmila Molkova --- content/en/blog/2024/otel-generative-ai/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index e63731316567..e67d3b485591 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -3,7 +3,7 @@ title: OpenTelemetry for Generative AI linkTitle: OpenTelemetry for Generative AI date: 2024-11-09 author: >- - [Drew Robbins](https://github.com/drewby) (Microsoft), [Luidmila + [Drew Robbins](https://github.com/drewby) (Microsoft), [Liudmila Molkova](https://github.com/lmolkova) (Microsoft) --- From 67f2bc98ec073c83d29564401d674acb07c74f45 Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Sat, 9 Nov 2024 10:52:40 +0900 Subject: [PATCH 05/32] Add link to Aspire Dashboard Co-authored-by: Liudmila Molkova --- content/en/blog/2024/otel-generative-ai/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index e67d3b485591..669acd8415e0 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -66,7 +66,7 @@ response = client.chat.completions.create( # The library captures telemetry, including request and response metadata, token usage, and more. ``` -With this simple instrumentation, one can begin capture traces from their generative AI application. Here is an example from the Aspire Dashboard for local debugging. +With this simple instrumentation, one can begin capture traces from their generative AI application. Here is an example from the [Aspire Dashboard](https://learn.microsoft.com/dotnet/aspire/fundamentals/dashboard/standalone?tabs=bash) for local debugging. ![Chat trace in Aspire Dashboard](aspire_dashboard_trace.png) From 6a51e0ca0c0004ccf24566f8732a1502272ef380 Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Sat, 9 Nov 2024 10:52:59 +0900 Subject: [PATCH 06/32] Add link to Jaeger Co-authored-by: Liudmila Molkova --- content/en/blog/2024/otel-generative-ai/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/en/blog/2024/otel-generative-ai/index.md b/content/en/blog/2024/otel-generative-ai/index.md index 669acd8415e0..18471b666cf2 100644 --- a/content/en/blog/2024/otel-generative-ai/index.md +++ b/content/en/blog/2024/otel-generative-ai/index.md @@ -70,7 +70,7 @@ With this simple instrumentation, one can begin capture traces from their genera ![Chat trace in Aspire Dashboard](aspire_dashboard_trace.png) -Here is a similar trace captured in Jaeger: +Here is a similar trace captured in [Jaeger](https://www.jaegertracing.io/docs/next-release-v2/getting-started/#running): ![Chat trace in Jaeger](jaeger_trace.png) From c66f6b46e25aa2f9544c436abcb5cb2cf5ec6f23 Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Sat, 9 Nov 2024 01:47:33 +0000 Subject: [PATCH 07/32] Add words to cspell --- .cspell/en-words.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.cspell/en-words.txt b/.cspell/en-words.txt index aa514245f43a..afe2fdef996f 100644 --- a/.cspell/en-words.txt +++ b/.cspell/en-words.txt @@ -144,3 +144,6 @@ wordpress WSGI zend zipkin +Liudmila +Molkova +GENAI \ No newline at end of file From fdef792a449c089ca8a775af6be2e25b1e0f18e3 Mon Sep 17 00:00:00 2001 From: Drew Robbins Date: Sat, 9 Nov 2024 01:56:45 +0000 Subject: [PATCH 08/32] Updates from PR review --- .../aspire-dashboard-content-capture.png | Bin 0 -> 138879 bytes .../aspire-dashboard-trace.png | Bin 0 -> 303555 bytes .../en/blog/2024/otel-generative-ai/index.md | 98 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