> ## Documentation Index
> Fetch the complete documentation index at: https://arize-ax.mintlify.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# LLMRunMetadata

> Ingest metadata about your LLM inferences

<CardGroup>
  <Card title="View source on Github" href="https://github.com/Arize-ai/client_python/blob/main/src/arize/utils/types.py" icon="github" horizontal />
</CardGroup>

Arize class to map up to 4 columns: `total_token_count_column_name` , `prompt_token_count_column_name`, `response_token_count_column_name`, and`response_latency_ms_column_name`

```python theme={null}
class LLMRunMetadata:
    total_token_count: Optional[int] = None
    prompt_token_count: Optional[int] = None
    response_token_count: Optional[int] = None
    response_latency_ms: Optional[Union[int,float]] = None
```

| Parameters             | Data Type    | Description                                                                                              |
| ---------------------- | ------------ | -------------------------------------------------------------------------------------------------------- |
| `total_token_count`    | int          | The total number of tokens used in the inference, both in the prompt sent to the LLM and in its response |
| `promt_token_count`    | int          | The number of tokens used in the prompt sent to the LLM                                                  |
| `response_token_count` | int          | The number of tokens used in the response returned by the LLM                                            |
| `response_latency_ms`  | int or float | The latency (in ms) experienced during the LLM run                                                       |

### Code Example

```python theme={null}
from arize.utils.types import LLMRunMetadata

# Declare LLM run metadata
llm_run_metadata = LLMRunMetadata(
    total_token_count = 4325,
    prompt_token_count = 2325,
    response_token_count = 2000,
    response_latency_ms = 20000,
)
```