semantic-conventions/docs/gen-ai/gen-ai-events.md

Semantic conventions for generative AI events

Status: Development

• Event: gen_ai.system.message
• Event: gen_ai.user.message
• Event: gen_ai.assistant.message
• Event: gen_ai.tool.message
• Event: gen_ai.choice
• Custom events
• Examples
  • Chat completion
  • Tools
  • Chat completion with multiple choices

[!Warning]

Existing GenAI instrumentations that are using v1.36.0 of this document (or prior):

• SHOULD NOT change the version of the GenAI conventions that they emit by default. Conventions include, but are not limited to, attributes, metric, span and event names, span kind and unit of measure.
• SHOULD introduce an environment variable OTEL_SEMCONV_STABILITY_OPT_IN as a comma-separated list of category-specific values. The list of values includes:
  • gen_ai_latest_experimental - emit the latest experimental version of GenAI conventions (supported by the instrumentation) and do not emit the old one (v1.36.0 or prior).
  • The default behavior is to continue emitting whatever version of the GenAI conventions the instrumentation was emitting (1.36.0 or prior).

This transition plan will be updated to include stable version before the GenAI conventions are marked as stable.

GenAI instrumentations MAY capture user inputs sent to the model and responses received from it as events.

Note: Events are in-development and not yet available in some languages. Check spec-compliance matrix to see the implementation status in corresponding language.

Instrumentations MAY capture inputs and outputs if and only if application has enabled the collection of this data. This is for three primary reasons:

1. Data privacy concerns. End users of GenAI applications may input sensitive information or personally identifiable information (PII) that they do not wish to be sent to a telemetry backend.
2. Data size concerns. Although there is no specified limit to sizes, there are practical limitations in programming languages and telemetry systems. Some GenAI systems allow for extremely large context windows that end users may take full advantage of.
3. Performance concerns. Sending large amounts of data to a telemetry backend may cause performance issues for the application.

Body fields that contain user input, model output, or other potentially sensitive and verbose data SHOULD NOT be captured by default.

Semantic conventions for individual systems which extend content events SHOULD document all additional body fields and specify whether they should be captured by default or need application to opt into capturing them.

Telemetry consumers SHOULD expect to receive unknown body fields.

Instrumentations SHOULD NOT capture undocumented body fields and MUST follow the documented defaults for known fields. Instrumentations MAY offer configuration options allowing to disable events or allowing to capture all fields.

Event: gen_ai.system.message

Status:

The event name MUST be gen_ai.system.message.

This event describes the system instructions passed to the GenAI model.

Attribute	Type	Description	Examples	Requirement Level	Stability
gen_ai.provider.name	string	The Generative AI provider as identified by the client or server instrumentation. [1]	openai; gcp.gen_ai; gcp.vertex_ai	Recommended

[1] gen_ai.provider.name: The attribute SHOULD be set based on the instrumentation's best knowledge and may differ from the actual model provider.

Multiple providers, including Azure OpenAI, Gemini, and AI hosting platforms are accessible using the OpenAI REST API and corresponding client libraries, but may proxy or host models from different providers.

The gen_ai.request.model, gen_ai.response.model, and server.address attributes may help identify the actual system in use.

The gen_ai.provider.name attribute acts as a discriminator that identifies the GenAI telemetry format flavor specific to that provider within GenAI semantic conventions. It SHOULD be set consistently with provider-specific attributes and signals. For example, GenAI spans, metrics, and events related to AWS Bedrock should have the gen_ai.provider.name set to aws.bedrock and include applicable aws.bedrock.* attributes and are not expected to include openai.* attributes.

---

gen_ai.provider.name has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description	Stability
anthropic	Anthropic
aws.bedrock	AWS Bedrock
azure.ai.inference	Azure AI Inference
azure.ai.openai	Azure OpenAI
cohere	Cohere
deepseek	DeepSeek
gcp.gemini	Gemini [2]
gcp.gen_ai	Any Google generative AI endpoint [3]
gcp.vertex_ai	Vertex AI [4]
groq	Groq
ibm.watsonx.ai	IBM Watsonx AI
mistral_ai	Mistral AI
openai	OpenAI
perplexity	Perplexity
x_ai	xAI

[2]: Used when accessing the 'generativelanguage.googleapis.com' endpoint. Also known as the AI Studio API.

[3]: May be used when specific backend is unknown.

[4]: Used when accessing the 'aiplatform.googleapis.com' endpoint.

Body fields:

⚠️ Body fields will be moved to complex attributes once the semantic convention tooling supports complex attributes (see #1870).

Body Field	Type	Description	Examples	Requirement Level	Stability
content	undefined	The contents of the system message.	You're a helpful bot	Opt-In
role	string	The actual role of the message author as passed in the message.	system; instruction	Conditionally Required if available and not equal to system.

Event: gen_ai.user.message

Status:

The event name MUST be gen_ai.user.message.

This event describes the user message passed to the GenAI model.

Attribute	Type	Description	Examples	Requirement Level	Stability
gen_ai.provider.name	string	The Generative AI provider as identified by the client or server instrumentation. [1]	openai; gcp.gen_ai; gcp.vertex_ai	Recommended

[1] gen_ai.provider.name: The attribute SHOULD be set based on the instrumentation's best knowledge and may differ from the actual model provider.

Multiple providers, including Azure OpenAI, Gemini, and AI hosting platforms are accessible using the OpenAI REST API and corresponding client libraries, but may proxy or host models from different providers.

The gen_ai.request.model, gen_ai.response.model, and server.address attributes may help identify the actual system in use.

The gen_ai.provider.name attribute acts as a discriminator that identifies the GenAI telemetry format flavor specific to that provider within GenAI semantic conventions. It SHOULD be set consistently with provider-specific attributes and signals. For example, GenAI spans, metrics, and events related to AWS Bedrock should have the gen_ai.provider.name set to aws.bedrock and include applicable aws.bedrock.* attributes and are not expected to include openai.* attributes.

---

gen_ai.provider.name has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description	Stability
anthropic	Anthropic
aws.bedrock	AWS Bedrock
azure.ai.inference	Azure AI Inference
azure.ai.openai	Azure OpenAI
cohere	Cohere
deepseek	DeepSeek
gcp.gemini	Gemini [2]
gcp.gen_ai	Any Google generative AI endpoint [3]
gcp.vertex_ai	Vertex AI [4]
groq	Groq
ibm.watsonx.ai	IBM Watsonx AI
mistral_ai	Mistral AI
openai	OpenAI
perplexity	Perplexity
x_ai	xAI

[2]: Used when accessing the 'generativelanguage.googleapis.com' endpoint. Also known as the AI Studio API.

[3]: May be used when specific backend is unknown.

[4]: Used when accessing the 'aiplatform.googleapis.com' endpoint.

Body fields:

⚠️ Body fields will be moved to complex attributes once the semantic convention tooling supports complex attributes (see #1870).

Body Field	Type	Description	Examples	Requirement Level	Stability
content	undefined	The contents of the user message.	What's the weather in Paris?	Opt-In
role	string	The actual role of the message author as passed in the message.	user; customer	Conditionally Required if available and not equal to user.

Event: gen_ai.assistant.message

Status:

The event name MUST be gen_ai.assistant.message.

This event describes the assistant message passed to GenAI system.

Attribute	Type	Description	Examples	Requirement Level	Stability
gen_ai.provider.name	string	The Generative AI provider as identified by the client or server instrumentation. [1]	openai; gcp.gen_ai; gcp.vertex_ai	Recommended

[1] gen_ai.provider.name: The attribute SHOULD be set based on the instrumentation's best knowledge and may differ from the actual model provider.

Multiple providers, including Azure OpenAI, Gemini, and AI hosting platforms are accessible using the OpenAI REST API and corresponding client libraries, but may proxy or host models from different providers.

The gen_ai.request.model, gen_ai.response.model, and server.address attributes may help identify the actual system in use.

The gen_ai.provider.name attribute acts as a discriminator that identifies the GenAI telemetry format flavor specific to that provider within GenAI semantic conventions. It SHOULD be set consistently with provider-specific attributes and signals. For example, GenAI spans, metrics, and events related to AWS Bedrock should have the gen_ai.provider.name set to aws.bedrock and include applicable aws.bedrock.* attributes and are not expected to include openai.* attributes.

---

gen_ai.provider.name has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description	Stability
anthropic	Anthropic
aws.bedrock	AWS Bedrock
azure.ai.inference	Azure AI Inference
azure.ai.openai	Azure OpenAI
cohere	Cohere
deepseek	DeepSeek
gcp.gemini	Gemini [2]
gcp.gen_ai	Any Google generative AI endpoint [3]
gcp.vertex_ai	Vertex AI [4]
groq	Groq
ibm.watsonx.ai	IBM Watsonx AI
mistral_ai	Mistral AI
openai	OpenAI
perplexity	Perplexity
x_ai	xAI

[2]: Used when accessing the 'generativelanguage.googleapis.com' endpoint. Also known as the AI Studio API.

[3]: May be used when specific backend is unknown.

[4]: Used when accessing the 'aiplatform.googleapis.com' endpoint.

Body fields:

⚠️ Body fields will be moved to complex attributes once the semantic convention tooling supports complex attributes (see #1870).

Body Field	Type	Description	Examples	Requirement Level	Stability
content	undefined	The contents of the tool message.	The weather in Paris is rainy and overcast, with temperatures around 57°F	Opt-In
role	string	The actual role of the message author as passed in the message.	assistant; bot	Conditionally Required if available and not equal to assistant.
tool_calls:	map[]	The tool calls generated by the model, such as function calls.		Conditionally Required if available
function:	map	The function call.		Required
arguments	undefined	The arguments of the function as provided in the LLM response. [1]	{\"location\": \"Paris\"}	Opt-In
name	string	The name of the function.	get_weather	Required
id	string	The id of the tool call.	call_mszuSIzqtI65i1wAUOE8w5H4	Required
type	enum	The type of the tool.	function	Required

[1]: Models usually return arguments as a JSON string. In this case, it's RECOMMENDED to provide arguments as is without attempting to deserialize them. Semantic conventions for individual systems MAY specify a different type for arguments field.

type has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description	Stability
function	Function

Event: gen_ai.tool.message

Status:

The event name MUST be gen_ai.tool.message.

This event describes the response from a tool or function call passed to the GenAI model.

Attribute	Type	Description	Examples	Requirement Level	Stability
gen_ai.provider.name	string	The Generative AI provider as identified by the client or server instrumentation. [1]	openai; gcp.gen_ai; gcp.vertex_ai	Recommended

[1] gen_ai.provider.name: The attribute SHOULD be set based on the instrumentation's best knowledge and may differ from the actual model provider.

Multiple providers, including Azure OpenAI, Gemini, and AI hosting platforms are accessible using the OpenAI REST API and corresponding client libraries, but may proxy or host models from different providers.

The gen_ai.request.model, gen_ai.response.model, and server.address attributes may help identify the actual system in use.

The gen_ai.provider.name attribute acts as a discriminator that identifies the GenAI telemetry format flavor specific to that provider within GenAI semantic conventions. It SHOULD be set consistently with provider-specific attributes and signals. For example, GenAI spans, metrics, and events related to AWS Bedrock should have the gen_ai.provider.name set to aws.bedrock and include applicable aws.bedrock.* attributes and are not expected to include openai.* attributes.

---

gen_ai.provider.name has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description	Stability
anthropic	Anthropic
aws.bedrock	AWS Bedrock
azure.ai.inference	Azure AI Inference
azure.ai.openai	Azure OpenAI
cohere	Cohere
deepseek	DeepSeek
gcp.gemini	Gemini [2]
gcp.gen_ai	Any Google generative AI endpoint [3]
gcp.vertex_ai	Vertex AI [4]
groq	Groq
ibm.watsonx.ai	IBM Watsonx AI
mistral_ai	Mistral AI
openai	OpenAI
perplexity	Perplexity
x_ai	xAI

[2]: Used when accessing the 'generativelanguage.googleapis.com' endpoint. Also known as the AI Studio API.

[3]: May be used when specific backend is unknown.

[4]: Used when accessing the 'aiplatform.googleapis.com' endpoint.

Body fields:

⚠️ Body fields will be moved to complex attributes once the semantic convention tooling supports complex attributes (see #1870).

Body Field	Type	Description	Examples	Requirement Level	Stability
content	undefined	The contents of the tool message.	rainy, 57°F	Opt-In
id	string	Tool call id that this message is responding to.	call_mszuSIzqtI65i1wAUOE8w5H4	Required
role	string	The actual role of the message author as passed in the message.	tool; function	Conditionally Required if available and not equal to tool.

Event: gen_ai.choice

Status:

The event name MUST be gen_ai.choice.

This event describes the Gen AI response message.

Attribute	Type	Description	Examples	Requirement Level	Stability
gen_ai.provider.name	string	The Generative AI provider as identified by the client or server instrumentation. [1]	openai; gcp.gen_ai; gcp.vertex_ai	Recommended

[1] gen_ai.provider.name: The attribute SHOULD be set based on the instrumentation's best knowledge and may differ from the actual model provider.

Multiple providers, including Azure OpenAI, Gemini, and AI hosting platforms are accessible using the OpenAI REST API and corresponding client libraries, but may proxy or host models from different providers.

The gen_ai.request.model, gen_ai.response.model, and server.address attributes may help identify the actual system in use.

The gen_ai.provider.name attribute acts as a discriminator that identifies the GenAI telemetry format flavor specific to that provider within GenAI semantic conventions. It SHOULD be set consistently with provider-specific attributes and signals. For example, GenAI spans, metrics, and events related to AWS Bedrock should have the gen_ai.provider.name set to aws.bedrock and include applicable aws.bedrock.* attributes and are not expected to include openai.* attributes.

---

gen_ai.provider.name has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description	Stability
anthropic	Anthropic
aws.bedrock	AWS Bedrock
azure.ai.inference	Azure AI Inference
azure.ai.openai	Azure OpenAI
cohere	Cohere
deepseek	DeepSeek
gcp.gemini	Gemini [2]
gcp.gen_ai	Any Google generative AI endpoint [3]
gcp.vertex_ai	Vertex AI [4]
groq	Groq
ibm.watsonx.ai	IBM Watsonx AI
mistral_ai	Mistral AI
openai	OpenAI
perplexity	Perplexity
x_ai	xAI

[2]: Used when accessing the 'generativelanguage.googleapis.com' endpoint. Also known as the AI Studio API.

[3]: May be used when specific backend is unknown.

[4]: Used when accessing the 'aiplatform.googleapis.com' endpoint.

Body fields:

⚠️ Body fields will be moved to complex attributes once the semantic convention tooling supports complex attributes (see #1870).

Body Field	Type	Description	Examples	Requirement Level	Stability
finish_reason	enum	The reason the model stopped generating tokens.	stop; tool_calls; content_filter	Required
index	int	The index of the choice in the list of choices.	0; 1	Required
message:	map	GenAI response message.		Recommended
content	undefined	The contents of the assistant message.	The weather in Paris is rainy and overcast, with temperatures around 57°F	Opt-In
role	string	The actual role of the message author as passed in the message.	assistant; bot	Conditionally Required if available and not equal to assistant.
tool_calls:	map[]	The tool calls generated by the model, such as function calls.		Conditionally Required if available
function:	map	The function that the model called.		Required
arguments	undefined	The arguments of the function as provided in the LLM response. [1]	{\"location\": \"Paris\"}	Opt-In
name	string	The name of the function.	get_weather	Required
id	string	The id of the tool call.	call_mszuSIzqtI65i1wAUOE8w5H4	Required
type	enum	The type of the tool.	function	Required

[1]: Models usually return arguments as a JSON string. In this case, it's RECOMMENDED to provide arguments as is without attempting to deserialize them. Semantic conventions for individual systems MAY specify a different type for arguments field.

finish_reason has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description	Stability
content_filter	Content Filter
error	Error
length	Length
stop	Stop
tool_calls	Tool Calls

type has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description	Stability
function	Function

Custom events

System-specific events that are not covered in this document SHOULD be documented in corresponding Semantic Conventions extensions and SHOULD follow {gen_ai.provider.name}.* naming pattern.

Examples

Chat completion

This is an example of telemetry generated for a chat completion call with system and user messages.

%%{init:
{
  "sequence": { "messageAlign": "left", "htmlLabels":true },
  "themeVariables": { "noteBkgColor" : "green", "noteTextColor": "black", "activationBkgColor": "green", "htmlLabels":true }
}
}%%
sequenceDiagram
    participant A as Application
    participant I as Instrumented Client
    participant M as Model
    A->>+I: #U+200D
    I->>M: gen_ai.system.message: You are a helpful bot<br/>gen_ai.user.message: Tell me a joke about OpenTelemetry
    Note left of I: GenAI Client span
    I-->M: gen_ai.choice: Why did the developer bring OpenTelemetry to the party? Because it always knows how to trace the fun!
    I-->>-A: #U+200D

GenAI Client span:

Attribute name	Value
Span name	"chat gpt-4"
gen_ai.provider.name	"openai"
gen_ai.request.model	"gpt-4"
gen_ai.request.max_tokens	200
gen_ai.request.top_p	1.0
gen_ai.response.id	"chatcmpl-9J3uIL87gldCFtiIbyaOvTeYBRA3l"
gen_ai.response.model	"gpt-4-0613"
gen_ai.usage.output_tokens	47
gen_ai.usage.input_tokens	52
gen_ai.response.finish_reasons	["stop"]

Events:

1. gen_ai.system.message

   Property	Value
   gen_ai.provider.name	"openai"
   Event body (with content enabled)	{"content": "You're a helpful bot"}

2. gen_ai.user.message

   Property	Value
   gen_ai.provider.name	"openai"
   Event body (with content enabled)	{"content":"Tell me a joke about OpenTelemetry"}

3. gen_ai.choice

   Property	Value
   gen_ai.provider.name	"openai"
   Event body (with content enabled)	{"index":0,"finish_reason":"stop","message":{"content":"Why did the developer bring OpenTelemetry to the party? Because it always knows how to trace the fun!"}}
   Event body (without content)	{"index":0,"finish_reason":"stop","message":{}}

Tools

This is an example of telemetry generated for a chat completion call with user message and function definition that results in a model requesting application to call provided function. Application executes a function and requests another completion now with the tool response.

%%{init:
{
  "sequence": { "messageAlign": "left", "htmlLabels":true },
  "themeVariables": { "noteBkgColor" : "green", "noteTextColor": "black", "activationBkgColor": "green", "htmlLabels":true }
}
}%%
sequenceDiagram
    participant A as Application
    participant I as Instrumented Client
    participant M as Model
    A->>+I: #U+200D
    I->>M: gen_ai.user.message: What's the weather in Paris?
    Note left of I: GenAI Client span 1
    I-->M: gen_ai.choice: Call to the get_weather tool with Paris as the location argument.
    I-->>-A: #U+200D
    A -->> A: parse tool parameters<br/>execute tool<br/>update chat history
    A->>+I: #U+200D
    I->>M: gen_ai.user.message: What's the weather in Paris?<br/>gen_ai.assistant.message: get_weather tool call<br/>gen_ai.tool.message: rainy, 57°F
    Note left of I: GenAI Client span 2
    I-->M: gen_ai.choice: The weather in Paris is rainy and overcast, with temperatures around 57°F
    I-->>-A: #U+200D

Here's the telemetry generated for each step in this scenario:

GenAI Client span 1:

Attribute name	Value
Span name	"chat gpt-4"
gen_ai.provider.name	"openai"
gen_ai.request.model	"gpt-4"
gen_ai.request.max_tokens	200
gen_ai.request.top_p	1.0
gen_ai.response.id	"chatcmpl-9J3uIL87gldCFtiIbyaOvTeYBRA3l"
gen_ai.response.model	"gpt-4-0613"
gen_ai.usage.output_tokens	17
gen_ai.usage.input_tokens	47
gen_ai.response.finish_reasons	["tool_calls"]

Events:

All the following events are parented to the GenAI chat span 1.

1. gen_ai.user.message (not reported when capturing content is disabled)

   Property	Value
   gen_ai.provider.name	"openai"
   Event body	{"content":"What's the weather in Paris?"}

2. gen_ai.choice

   Property	Value
   gen_ai.provider.name	"openai"
   Event body (with content)	{"index":0,"finish_reason":"tool_calls","message":{"tool_calls":[{"id":"call_VSPygqKTWdrhaFErNvMV18Yl","function":{"name":"get_weather","arguments":"{\"location\":\"Paris\"}"},"type":"function"}]}
   Event body (without content)	{"index":0,"finish_reason":"tool_calls","message":{"tool_calls":[{"id":"call_VSPygqKTWdrhaFErNvMV18Yl","function":{"name":"get_weather"},"type":"function"}]}

GenAI Client span 2:

Attribute name	Value
Span name	"chat gpt-4"
gen_ai.provider.name	"openai"
gen_ai.request.model	"gpt-4"
gen_ai.request.max_tokens	200
gen_ai.request.top_p	1.0
gen_ai.response.id	"chatcmpl-call_VSPygqKTWdrhaFErNvMV18Yl"
gen_ai.response.model	"gpt-4-0613"
gen_ai.usage.output_tokens	52
gen_ai.usage.input_tokens	47
gen_ai.response.finish_reasons	["stop"]

Events:

All the following events are parented to the GenAI chat span 2.

In this example, the event content matches the original messages, but applications may also drop messages or change their content.

1. gen_ai.user.message

   Property	Value
   gen_ai.provider.name	"openai"
   Event body	{"content":"What's the weather in Paris?"}

2. gen_ai.assistant.message

   Property	Value
   gen_ai.provider.name	"openai"
   Event body (content enabled)	{"tool_calls":[{"id":"call_VSPygqKTWdrhaFErNvMV18Yl","function":{"name":"get_weather","arguments":"{\"location\":\"Paris\"}"},"type":"function"}]}
   Event body (content not enabled)	{"tool_calls":[{"id":"call_VSPygqKTWdrhaFErNvMV18Yl","function":{"name":"get_weather"},"type":"function"}]}

3. gen_ai.tool.message

   Property	Value
   gen_ai.provider.name	"openai"
   Event body (content enabled)	{"content":"rainy, 57°F","id":"call_VSPygqKTWdrhaFErNvMV18Yl"}
   Event body (content not enabled)	{"id":"call_VSPygqKTWdrhaFErNvMV18Yl"}

4. gen_ai.choice

   Property	Value
   gen_ai.provider.name	"openai"
   Event body (content enabled)	{"index":0,"finish_reason":"stop","message":{"content":"The weather in Paris is rainy and overcast, with temperatures around 57°F"}}
   Event body (content not enabled)	{"index":0,"finish_reason":"stop","message":{}}

Chat completion with multiple choices

This example covers the scenario when user requests model to generate two completions for the same prompt :

%%{init:
{
  "sequence": { "messageAlign": "left", "htmlLabels":true },
  "themeVariables": { "noteBkgColor" : "green", "noteTextColor": "black", "activationBkgColor": "green", "htmlLabels":true }
}
}%%
sequenceDiagram
    participant A as Application
    participant I as Instrumented Client
    participant M as Model
    A->>+I: #U+200D
    I->>M: gen_ai.system.message - "You are a helpful bot"<br/>gen_ai.user.message - "Tell me a joke about OpenTelemetry"
    Note left of I: GenAI Client span
    I-->M: gen_ai.choice - Why did the developer bring OpenTelemetry to the party? Because it always knows how to trace the fun!<br/>gen_ai.choice - Why did OpenTelemetry get promoted? It had great span of control!
    I-->>-A: #U+200D

GenAI Client Span:

Attribute name	Value
Span name	"chat gpt-4"
gen_ai.provider.name	"openai"
gen_ai.request.model	"gpt-4"
gen_ai.request.max_tokens	200
gen_ai.request.top_p	1.0
gen_ai.response.id	"chatcmpl-9J3uIL87gldCFtiIbyaOvTeYBRA3l"
gen_ai.response.model	"gpt-4-0613"
gen_ai.usage.output_tokens	77
gen_ai.usage.input_tokens	52
gen_ai.response.finish_reasons	["stop", "stop"]

Events:

All events are parented to the GenAI chat span above.

1. gen_ai.system.message: the same as in the Chat Completion example

2. gen_ai.user.message: the same as in the Chat Completion example

3. gen_ai.choice

   Property	Value
   gen_ai.provider.name	"openai"
   Event body (content enabled)	{"index":0,"finish_reason":"stop","message":{"content":"Why did the developer bring OpenTelemetry to the party? Because it always knows how to trace the fun!"}}

4. gen_ai.choice

   Property	Value
   gen_ai.provider.name	"openai"
   Event body (content enabled)	{"index":1,"finish_reason":"stop","message":{"content":"Why did OpenTelemetry get promoted? It had great span of control!"}}