> ## 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.

# LLMRunMetadataColumnNames

> Ingest metadata about your LLM inferences

<img className="inline m-0" src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/arize-docs-images/9e9834e4-image.jpeg" />[View source on Github](https://github.com/Arize-ai/client_python/blob/main/src/arize/utils/types.py)

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 LLMRunMetadataColumnNames:
    total_token_count_column_name: Optional[str] = None
    prompt_token_count_column_name: Optional[str] = None
    response_token_count_column_name: Optional[str] = None
    response_latency_ms_column_name: Optional[str] = None
```

| Parameters                         | Data Type | Expected Type in Column                                | Description                                                                                                              |
| ---------------------------------- | --------- | ------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------ |
| `total_token_count_column_name`    | str       | The contents of this column must be integers           | Column name for the total number of tokens used in the inference, both in the prompt sent to the LLM and in its response |
| `promt_token_count_column_name`    | str       | The contents of this column must be integers           | Column name for the number of tokens used in the prompt sent to the LLM                                                  |
| `response_token_count_column_name` | str       | The contents of this column must be integers           | Column name for the number of tokens used in the response returned by the LLM                                            |
| `response_latency_ms_column_name`  | str       | The contents of this column must be integers or floats | Column name for the latency (in ms) experienced during the LLM run                                                       |

### Code Example

| Index | total\_token\_count | prompt\_token\_count | response\_token\_count | response\_latency |
| ----- | ------------------- | -------------------- | ---------------------- | ----------------- |
| `0`   | `4325`              | `2325`               | `2000`                 | `20000`           |

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

# Declare LLM run metadata columns
llm_run_metadata = LLMRunMetadataColumnNames(
    total_token_count_column_name = "total_token_count", # column containing the number of tokens in the prompt and response
    prompt_token_count_column_name = "prompt_token_count", # column containing the number of tokens in the prompt
    response_token_count_column_name = "response_token_count", # column containing the number of tokens in the response
    response_latency_ms_column_name = "response_latency" # column containing the latency of the LLM run
)
```