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[chore] ensure gen_ai docs contain correctly formatted lists (#3077)
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docs/gen-ai/aws-bedrock.md

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@@ -37,7 +37,6 @@ for [Gen AI Spans](gen-ai-spans.md).
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These attributes track input data and metadata for a request to an AWS Bedrock model. The attributes include general Generative AI
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attributes and ones specific the AWS Bedrock.
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**[4] `gen_ai.conversation.id`:** Instrumentations SHOULD populate conversation id when they have it readily available
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for a given operation, for example:
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- when client framework being instrumented manages conversation history
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- when client framework being instrumented manages conversation history
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(see [LlamaIndex chat store](https://docs.llamaindex.ai/en/stable/module_guides/storing/chat_stores/))
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- when instrumenting GenAI client libraries that maintain conversation on the backend side
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(see [AWS Bedrock agent sessions](https://docs.aws.amazon.com/bedrock/latest/userguide/agents-session-state.html),
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[OpenAI Assistant threads](https://platform.openai.com/docs/api-reference/threads))
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[DocumentStatus]: https://opentelemetry.io/docs/specs/otel/document-status

docs/gen-ai/azure-ai-inference.md

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@@ -44,7 +44,6 @@ The Semantic Conventions for [Azure AI Inference](https://learn.microsoft.com/az
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`gen_ai.provider.name` MUST be set to `"azure.ai.inference"` and SHOULD be provided **at span creation time**.
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**[3] `gen_ai.conversation.id`:** Instrumentations SHOULD populate conversation id when they have it readily available
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for a given operation, for example:
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- when client framework being instrumented manages conversation history
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- when client framework being instrumented manages conversation history
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(see [LlamaIndex chat store](https://docs.llamaindex.ai/en/stable/module_guides/storing/chat_stores/))
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- when instrumenting GenAI client libraries that maintain conversation on the backend side
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(see [AWS Bedrock agent sessions](https://docs.aws.amazon.com/bedrock/latest/userguide/agents-session-state.html),
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[OpenAI Assistant threads](https://platform.openai.com/docs/api-reference/threads))
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**[4] `gen_ai.output.type`:** when applicable and if the request includes an output format.
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**[5] `gen_ai.output.type`:** This attribute SHOULD be set to the output type requested by the client:
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- `json` for structured outputs with defined or undefined schema
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- `image` for image output
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- `speech` for speech output
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### Embedding
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docs/gen-ai/gen-ai-agent-spans.md

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### Create agent span
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### Invoke agent span
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instrumented protocol such as HTTP.
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Examples of span kinds for different agent scenarios:
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- `CLIENT`: Remote agent services (e.g., OpenAI Assistants API, AWS Bedrock Agents)
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- `INTERNAL`: In-process agents (e.g., LangChain agents, CrewAI agents)
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**[4] `gen_ai.conversation.id`:** Instrumentations SHOULD populate conversation id when they have it readily available
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- when client framework being instrumented manages conversation history
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(see [LlamaIndex chat store](https://docs.llamaindex.ai/en/stable/module_guides/storing/chat_stores/))
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(see [AWS Bedrock agent sessions](https://docs.aws.amazon.com/bedrock/latest/userguide/agents-session-state.html),
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[OpenAI Assistant threads](https://platform.openai.com/docs/api-reference/threads))
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## Execute tool span
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docs/gen-ai/gen-ai-events.md

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## Event: `event.gen_ai.client.inference.operation.details`
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**[3] `gen_ai.conversation.id`:** Instrumentations SHOULD populate conversation id when they have it readily available
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(see [AWS Bedrock agent sessions](https://docs.aws.amazon.com/bedrock/latest/userguide/agents-session-state.html),
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[OpenAI Assistant threads](https://platform.openai.com/docs/api-reference/threads))
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## Event: `event.gen_ai.evaluation.result`
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[DocumentStatus]: https://opentelemetry.io/docs/specs/otel/document-status

docs/gen-ai/gen-ai-spans.md

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### Inference
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### Embeddings
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## Capturing instructions, inputs, and outputs
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docs/gen-ai/openai.md

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### Metric: `gen_ai.client.operation.duration`
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model/gen-ai/spans.yaml

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extends: attributes.gen_ai.inference.client

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