# LLM call examples

LLMS index: [llms.txt](/llms.txt)

---

## Simple chat completion

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

```mermaid
%%{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: input = [system: You are a helpful bot, user: Tell me a joke about OpenTelemetry]
    Note left of I: GenAI Client span
    I-->M: assistant: 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 when content capturing is disabled

| Property                         | Value                                      |
| -------------------------------- | ------------------------------------------ |
| Span name                        | `"chat gpt-4"`                             |
| Trace id                         | `"4bf92f3577b34da6a3ce929d0e0e4736"`       |
| Span id                          | `"00f067aa0ba902b7"`                       |
| `gen_ai.provider.name`           | `"openai"`                                 |
| `gen_ai.operation.name`          | `"chat"`                                   |
| `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"]`                                 |

### GenAI client span when content capturing is enabled on span attributes

| Property                         | Value                                                      |
| -------------------------------- | ---------------------------------------------------------- |
| Span name                        | `"chat gpt-4"`                                             |
| Trace id                         | `"4bf92f3577b34da6a3ce929d0e0e4736"`                       |
| Span id                          | `"00f067aa0ba902b7"`                                       |
| `gen_ai.provider.name`           | `"openai"`                                                 |
| `gen_ai.operation.name`          | `"chat"`                                                   |
| `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"]`                                                 |
| `gen_ai.input.messages`          | [`gen_ai.input.messages`](#gen-ai-input-messages-simple)   |
| `gen_ai.output.messages`         | [`gen_ai.output.messages`](#gen-ai-output-messages-simple) |

<span id="gen-ai-input-messages-simple">`gen_ai.input.messages` value</span>

```json
[
  {
    "role": "system",
    "parts": [
      {
        "type": "text",
        "content": "You are a helpful bot"
      }
    ]
  },
  {
    "role": "user",
    "parts": [
      {
        "type": "text",
        "content": "Tell me a joke about OpenTelemetry"
      }
    ]
  }
]
```

<span id="gen-ai-output-messages-simple">`gen_ai.output.messages` value</span>

```json
[
  {
    "role": "assistant",
    "parts": [
      {
        "type": "text",
        "content": " Why did the developer bring OpenTelemetry to the party? Because it always knows how to trace the fun!"
      }
    ],
    "finish_reason": "stop"
  }
]
```

### GenAI telemetry when content capturing is enabled on event attributes

Span:

| Property                         | Value                                      |
| -------------------------------- | ------------------------------------------ |
| Span name                        | `"chat gpt-4"`                             |
| Trace id                         | `"4bf92f3577b34da6a3ce929d0e0e4736"`       |
| Span id                          | `"00f067aa0ba902b7"`                       |
| `gen_ai.provider.name`           | `"openai"`                                 |
| `gen_ai.operation.name`          | `"chat"`                                   |
| `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"]`                                 |

Event:

| Property                         | Value                                                     |
| -------------------------------- | --------------------------------------------------------- |
| Trace id                         | `"4bf92f3577b34da6a3ce929d0e0e4736"`                      |
| Span id                          | `"00f067aa0ba902b7"`                                      |
| `gen_ai.provider.name`           | `"openai"`                                                |
| `gen_ai.operation.name`          | `"chat"`                                                  |
| `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"]`                                                |
| `gen_ai.input.messages`          | [`gen_ai.input.messages`](#gen-ai-input-messages-event)   |
| `gen_ai.output.messages`         | [`gen_ai.output.messages`](#gen-ai-output-messages-event) |

<span id="gen-ai-input-messages-event">`gen_ai.input.messages` value</span>

```json
[
  {
    "role": "system",
    "parts": [
      {
        "type": "text",
        "content": "You are a helpful bot"
      }
    ]
  },
  {
    "role": "user",
    "parts": [
      {
        "type": "text",
        "content": "Tell me a joke about OpenTelemetry"
      }
    ]
  }
]
```

<span id="gen-ai-output-messages-event">`gen_ai.output.messages` value</span>

```json
[
  {
    "role": "assistant",
    "parts": [
      {
        "type": "text",
        "content": " Why did the developer bring OpenTelemetry to the party? Because it always knows how to trace the fun!"
      }
    ],
    "finish_reason": "stop"
  }
]
```

## Multimodal chat completion

Multimodal chat completions follow the same sequence and telemetry structure as [simple chat
completion](#simple-chat-completion) above, but contain additional types of Parts in the
`gen_ai.input.messages` and `gen_ai.output.messages` span/event attributes:

- `blob` parts, which represent data sent inline to or from the model.
- `uri` parts, which represent a reference to a remote file by URI.
- `file` parts, which represent a reference to a pre-uploaded file by ID.

These parts contain an optional `modality` field to capture the general category of the
content, and an optional `mime_type` to capture the specific [IANA media
type](https://www.iana.org/assignments/media-types/media-types.xhtml) of the content, if known.
See the [normative JSON schema](/docs/specs/semconv/gen-ai/gen-ai-input-messages.json) for more details.

### Multimodal inputs example

```jsonc
[
  {
    "role": "user",
    "parts": [
      {
        "type": "text",
        "content": "What is in the attached data?"
      },
      // A image with a URI
      {
        "type": "uri",
        "modality": "image",
        "mime_type": "image/png",
        "uri": "https://raw.githubusercontent.com/open-telemetry/opentelemetry.io/refs/heads/main/static/img/logos/opentelemetry-horizontal-color.png"
      },
      // A video with a vendor specific URI
      {
        "type": "uri",
        "modality": "video",
        "mime_type": "video/mp4",
        "uri": "gs://my-bucket/my-video.mp4"
      },
      // An image with opaque file ID e.g. the OpenAI files api
      {
        "type": "file",
        "file_id": "provider_fileid_123"
      },
      // An image with unknown mime_type but known modality
      {
        "type": "file",
        "modality": "image",
        "file_id": "provider_fileid_123"
      },
      // An inline image
      {
        "type": "blob",
        "modality": "image",
        "mime_type": "image/png",
        "content": "aGVsbG8gd29ybGQgaW1hZ2luZSB0aGlzIGlzIGFuIGltYWdlCg=="
      },
      // Inline audio
      {
        "type": "blob",
        "modality": "audio",
        "mime_type": "audio/wav",
        "content": "aGVsbG8gd29ybGQgaW1hZ2luZSB0aGlzIGlzIGFuIGltYWdlCg=="
      }
    ]
  }
]
```

### Multimodal output example

```jsonc
[
  {
    "role": "assistant",
    "finish_reason": "stop",
    "parts": [
      // Model generated an inline image
      {
        "type": "blob",
        "modality": "image",
        "mime_type": "image/jpg",
        "content": "aGVsbG8gd29ybGQgaW1hZ2luZSB0aGlzIGlzIGFuIGltYWdlCg=="
      }
    ]
  }
]
```

## Tool calls (functions)

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.

```mermaid
%%{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: input = [user: What's the weather in Paris?]
    Note left of I: GenAI Client span 1
    I-->M: assistant: 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: input = [user: What's the weather in Paris?, assistant: get_weather tool call, tool: rainy, 57°F]
    Note left of I: GenAI Client span 2
    I-->M: assistant: The weather in Paris is rainy and overcast, with temperatures around 57°F
    I-->>-A: #U+200D
```

### GenAI client spans when content capturing is disabled

The relationship between below spans depends on how user application code is written.
They are likely to be siblings if there is an encompassing span.

**GenAI client span 1:**

| Property                         | Value                                      |
| -------------------------------- | ------------------------------------------ |
| Span name                        | `"chat gpt-4"`                             |
| `gen_ai.provider.name`           | `"openai"`                                 |
| `gen_ai.operation.name`          | `"chat"`                                   |
| `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"]`                           |

**Tool call:**

If tool call is [instrumented according to execute-tool span definition](/docs/specs/semconv/gen-ai/gen-ai-spans.md#execute-tool-span), it may look like

| Property                | Value                             |
| ----------------------- | --------------------------------- |
| Span name               | `"execute_tool get_weather"`      |
| `gen_ai.tool.call.id`   | `"call_VSPygqKTWdrhaFErNvMV18Yl"` |
| `gen_ai.tool.name`      | `"get_weather"`                   |
| `gen_ai.operation.name` | `"execute_tool"`                  |
| `gen_ai.tool.type`      | `"function"`                      |

**GenAI client span 2:**

| Property                         | 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`      | `97`                                       |
| `gen_ai.response.finish_reasons` | `["stop"]`                                 |

### GenAI client spans when content capturing is enabled on span attributes

The relationship between below spans depends on how user application code is written.
They are likely to be siblings if there is an encompassing span.

**GenAI client span 1:**

| Property                         | Value                                                                |
| -------------------------------- | -------------------------------------------------------------------- |
| Span name                        | `"chat gpt-4"`                                                       |
| `gen_ai.provider.name`           | `"openai"`                                                           |
| `gen_ai.operation.name`          | `"chat"`                                                             |
| `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"]`                                                     |
| `gen_ai.input.messages`          | [`gen_ai.input.messages`](#gen-ai-input-messages-tool-call-span-1)   |
| `gen_ai.output.messages`         | [`gen_ai.output.messages`](#gen-ai-output-messages-tool-call-span-1) |

<span id="gen-ai-input-messages-tool-call-span-1">`gen_ai.input.messages` value</span>

```json
[
  {
    "role": "user",
    "parts": [
      {
        "type": "text",
        "content": "Weather in Paris?"
      }
    ]
  }
]
```

<span id="gen-ai-output-messages-tool-call-span-1">`gen_ai.output.messages` value</span>

```json
[
  {
    "role": "assistant",
    "parts": [
      {
        "type": "tool_call",
        "id": "call_VSPygqKTWdrhaFErNvMV18Yl",
        "name": "get_weather",
        "arguments": {
          "location": "Paris"
        }
      }
    ],
    "finish_reason": "tool_call"
  }
]
```

**Tool call:**

If tool call is [instrumented according to execute-tool span definition](/docs/specs/semconv/gen-ai/gen-ai-spans.md#execute-tool-span), it may look like this:

| Property                | Value                             |
| ----------------------- | --------------------------------- |
| Span name               | `"execute_tool get_weather"`      |
| `gen_ai.tool.call.id`   | `"call_VSPygqKTWdrhaFErNvMV18Yl"` |
| `gen_ai.tool.name`      | `"get_weather"`                   |
| `gen_ai.operation.name` | `"execute_tool"`                  |
| `gen_ai.tool.type`      | `"function"`                      |

**GenAI client span 2:**

| Property                         | 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`      | `97`                                                                 |
| `gen_ai.response.finish_reasons` | `["stop"]`                                                           |
| `gen_ai.input.messages`          | [`gen_ai.input.messages`](#gen-ai-input-messages-tool-call-span-2)   |
| `gen_ai.output.messages`         | [`gen_ai.output.messages`](#gen-ai-output-messages-tool-call-span-2) |

<span id="gen-ai-input-messages-tool-call-span-2">`gen_ai.input.messages` value</span>

```json
[
  {
    "role": "user",
    "parts": [
      {
        "type": "text",
        "content": "Weather in Paris?"
      }
    ]
  },
  {
    "role": "assistant",
    "parts": [
      {
        "type": "tool_call",
        "id": "call_VSPygqKTWdrhaFErNvMV18Yl",
        "name": "get_weather",
        "arguments": {
          "location": "Paris"
        }
      }
    ]
  },
  {
    "role": "tool",
    "parts": [
      {
        "type": "tool_call_response",
        "id": "call_VSPygqKTWdrhaFErNvMV18Yl",
        "response": "rainy, 57°F"
      }
    ]
  }
]
```

<span id="gen-ai-output-messages-tool-call-span-2">`gen_ai.output.messages` value</span>

```json
[
  {
    "role": "assistant",
    "parts": [
      {
        "type": "text",
        "content": "The weather in Paris is currently rainy with a temperature of 57°F."
      }
    ],
    "finish_reason": "stop"
  }
]
```

## System instructions along with chat history (content enabled)

Some providers allow to provide instructions separately from the chat history provided in the inputs
or in addition to `system` (`developer`, etc) message provided in the input.

This example demonstrates en edge case when conflicting instructions are provided
to the OpenAI responses API. In this case instructions are recorded in the `gen_ai.system_instructions` attribute.

```py
response = openai.responses.create(
  model="gpt-4",
  instructions="You must never tell jokes",
  input=[{"role": "system", "content": "You are a helpful assistant"}, {"role": "user", "content": "Tell me a joke"}])
```

Span:

| Property                         | Value                                                            |
| -------------------------------- | ---------------------------------------------------------------- |
| Span name                        | `"chat gpt-4"`                                                   |
| `gen_ai.provider.name`           | `"openai"`                                                       |
| `gen_ai.operation.name`          | `"chat"`                                                         |
| `gen_ai.request.model`           | `"gpt-4"`                                                        |
| `gen_ai.response.id`             | `"chatcmpl-9J3uIL87gldCFtiIbyaOvTeYBRA3l"`                       |
| `gen_ai.response.model`          | `"gpt-4-0613"`                                                   |
| `gen_ai.usage.output_tokens`     | `10`                                                             |
| `gen_ai.usage.input_tokens`      | `28`                                                             |
| `gen_ai.response.finish_reasons` | `["stop"]`                                                       |
| `gen_ai.system_instructions`     | [`gen_ai.system_instructions`](#gen-ai-system-instructions)      |
| `gen_ai.input.messages`          | [`gen_ai.input.messages`](#gen-ai-input-messages-instructions)   |
| `gen_ai.output.messages`         | [`gen_ai.output.messages`](#gen-ai-output-messages-instructions) |

<span id="gen-ai-system-instructions">`gen_ai.system_instructions` value</span>

```json
[
  {
    "type": "text",
    "content": "You must never tell jokes"
  }
]
```

<span id="gen-ai-input-messages-instructions">`gen_ai.input.messages` value</span>

```json
[
  {
    "role": "system",
    "parts": [
      {
        "type": "text",
        "content": "You are a helpful bot"
      }
    ]
  },
  {
    "role": "user",
    "parts": [
      {
        "type": "text",
        "content": "Tell me a joke about OpenTelemetry"
      }
    ]
  }
]
```

<span id="gen-ai-output-messages-instructions">`gen_ai.output.messages` value</span>

```json
[
  {
    "role": "assistant",
    "parts": [
      {
        "type": "text",
        "content": "I'm sorry, but I can't assist with that"
      }
    ],
    "finish_reason": "stop"
  }
]
```

## Chat completion with reasoning (content enabled)

| Property                         | Value                                                         |
| -------------------------------- | ------------------------------------------------------------- |
| Span name                        | `"chat gpt-4"`                                                |
| Trace id                         | `"4bf92f3577b34da6a3ce929d0e0e4736"`                          |
| Span id                          | `"00f067aa0ba902b7"`                                          |
| `gen_ai.provider.name`           | `"openai"`                                                    |
| `gen_ai.operation.name`          | `"chat"`                                                      |
| `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"]`                                                    |
| `gen_ai.input.messages`          | [`gen_ai.input.messages`](#gen-ai-input-messages-reasoning)   |
| `gen_ai.output.messages`         | [`gen_ai.output.messages`](#gen-ai-output-messages-reasoning) |

<span id="gen-ai-input-messages-reasoning">`gen_ai.input.messages` value</span>

```json
[
  {
    "role": "system",
    "parts": [
      {
        "type": "text",
        "content": "You are a helpful bot"
      }
    ]
  },
  {
    "role": "user",
    "parts": [
      {
        "type": "text",
        "content": "Tell me a joke about OpenTelemetry"
      }
    ]
  }
]
```

<span id="gen-ai-output-messages-reasoning">`gen_ai.output.messages` value</span>

```json
[
  {
    "role": "assistant",
    "parts": [
      {
        "type": "reasoning",
        "content": "Alright, the user wants a joke about OpenTelemetry… Hmm, OpenTelemetry is all about distributed tracing and metrics, right? So maybe I can play with the word \"trace.\" That's a core concept — tracing requests through systems. But how do I make that funny? What if I take \"trace\" literally and apply it to something unexpected, like a party? If I personify OpenTelemetry as a tool that \"knows where the fun is,\" I can make a pun out of tracing requests vs. tracing enjoyment. Yeah, that could work — let me put it all together."
      },
      {
        "type": "text",
        "content": " Why did the developer bring OpenTelemetry to the party? Because it always knows how to trace the fun!"
      }
    ],
    "finish_reason": "stop"
  }
]
```

## Tool calls (built-in)

This is an example of telemetry generated for OpenAI Reponses API call with `code_interpreter` tool that results in
a model provider executing a tool and returning response along with tool call details. Built-in tools use
`server_tool_call` and `server_tool_call_response` part types with polymorphic content to separate them from
client-side function tool calls.

```py
response = client.responses.create(   
  model="gpt-4",
  input="Write Python code that generates a random number, executes it, and returns the result.",
  tools=[{"type": "code_interpreter"}],
  include=['code_interpreter_call.outputs'],
  tool_choice="required",
)
```

```mermaid
%%{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: input = [system: You are a helpful bot, user: Write Python code that generates a random number, executes it, and returns the result.]
  Note left of I: GenAI Client span
  I --> M: tool:code='import random ....'<br>assistant: The generated random number is 95.
  I -->>- A: #U+200D
```

**GenAI client span:**

| Property                         | Value                                                              |
| -------------------------------- | ------------------------------------------------------------------ |
| Span name                        | `"chat gpt-4"`                                                     |
| `gen_ai.provider.name`           | `"openai"`                                                         |
| `gen_ai.operation.name`          | `"chat"`                                                           |
| `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`     | `44`                                                               |
| `gen_ai.usage.input_tokens`      | `385`                                                              |
| `gen_ai.response.finish_reasons` | `["stop"]`                                                         |
| `gen_ai.input.messages`          | [`gen_ai.input.messages`](#gen-ai-input-messages-built-in-tools)   |
| `gen_ai.output.messages`         | [`gen_ai.output.messages`](#gen-ai-output-messages-built-in-tools) |

<span id="gen-ai-input-messages-built-in-tools">`gen_ai.input.messages` value</span>

```json
[
  {
    "role": "system",
    "parts": [
      {
        "type": "text",
        "content": "You are a helpful bot"
      }
    ]
  },
  {
    "role": "user",
    "parts": [
      {
        "type": "text",
        "content": "Write Python code that generates a random number, executes it, and returns the result."
      }
    ]
  }
]
```

<span id="gen-ai-output-messages-built-in-tools">`gen_ai.output.messages` value</span>

```json
[
  {
    "role": "assistant",
    "parts": [
      {
        "type": "server_tool_call",
        "id": "call_VSPygqKTWdrhaFErNvMV18Yl",
        "name": "code_interpreter",
        "server_tool_call": {
          "type": "code_interpreter",
          "code": "import random\n\n# Generate a random number\nrandom_number = random.randint(1, 100)\n\n# Execute some operation with the random number (e.g., squaring it)\nresult = random_number ** 2\n\nrandom_number, result",
          "container_id": "cntr_690bdbfed8688190884efd4c7ae6435b0db1f006442e8941"
        }
      },
      {
        "type": "server_tool_call_response",
        "id": "call_VSPygqKTWdrhaFErNvMV18Yl",
        "server_tool_call_response": {
          "type": "code_interpreter",
          "outputs": [
            {
              "type": "logs",
              "logs": "(10, 20)"
            }
          ]
        }
      },
      {
        "type": "text",
        "content": "The generated random number is **89**, and the result of squaring it is **7921**"
      }
    ],
    "finish_reason": "stop"
  }
]
```

## Chat completion with multiple choices

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

```mermaid
%%{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: input = [system: You are a helpful bot, user: Tell me a joke about OpenTelemetry]
    Note left of I: GenAI Client span
    I-->M: assistant: Why did the developer bring OpenTelemetry to the party? Because it always knows how to trace the fun!<br/> assistant: Why did OpenTelemetry get promoted? It had great span of control!
    I-->>-A: #U+200D
```

### GenAI client span when content capturing is enabled on span attributes

| Property                         | Value                                                       |
| -------------------------------- | ----------------------------------------------------------- |
| Span name                        | `"chat gpt-4"`                                              |
| `gen_ai.provider.name`           | `"openai"`                                                  |
| `gen_ai.operation.name`          | `"chat"`                                                    |
| `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"]`                                          |
| `gen_ai.input.messages`          | [`gen_ai.input.messages`](#gen-ai-input-messages-choices)   |
| `gen_ai.output.messages`         | [`gen_ai.output.messages`](#gen-ai-output-messages-choices) |

<span id="gen-ai-input-messages-choices">`gen_ai.input.messages` value</span>

```json
[
  {
    "role": "system",
    "parts": [
      {
        "type": "text",
        "content": "You are a helpful bot"
      }
    ]
  },
  {
    "role": "user",
    "parts": [
      {
        "type": "text",
        "content": "Tell me a joke about OpenTelemetry"
      }
    ]
  }
]
```

<span id="gen-ai-output-messages-choices">`gen_ai.output.messages` value</span>

```json
[
  {
    "role": "assistant",
    "parts": [
      {
        "type": "text",
        "content": " Why did the developer bring OpenTelemetry to the party? Because it always knows how to trace the fun!"
      }
    ],
    "finish_reason": "stop"
  },
  {
    "role": "assistant",
    "parts": [
      {
        "type": "text",
        "content": " Why did OpenTelemetry get promoted? It had great span of control!"
      }
    ],
    "finish_reason": "stop"
  }
]
```