> ## 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.
# Log
Arize method to log model data record-by-record.
```python theme={null}
log(
model_id: str,
model_type: ModelTypes,
environment: Environments,
model_version: Optional[str] = None,
prediction_id: Optional[Union[str, int, float]] = None,
prediction_timestamp: Optional[int] = None,
prediction_label: Optional[PredictionLabelTypes] = None,
actual_label: Optional[ActualLabelTypes] = None,
features: Optional[Dict[str, Union[str, bool, float, int, List[str], TypedValue]]] = None,
embedding_features: Optional[Dict[str, Embedding]] = None,
shap_values: Optional[Dict[str, float]] = None,
tags: Optional[Dict[str, Union[str, bool, float, int, TypedValue]]] = None,
batch_id: Optional[str] = None,
prompt: Optional[Union[str, Embedding]] = None,
response: Optional[Union[str, Embedding]] = None,
prompt_template: Optional[str] = None,
prompt_template_version: Optional[str] = None,
llm_model_name: Optional[str] = None,
llm_params: Optional[Dict[str, Union[str, bool, float, int]]] = None,
llm_run_metadata: Optional[LLMRunMetadata] = None,
)
```
| Parameter | Data Type | Description |
| :------------------------ | :------------------------------------------------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `model_id` | `str` | (Required) A unique name to identify your model in the Arize platform. |
| `model_type` | [arize.utils.types.ModelTypes](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-log/utils.types.modeltypes) | (Required\*) Declares what model type this prediction/actual is for \*(as of v.5.X.X). |
| `environment` | [arize.utils.types.Environments](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-log/utils.types.environments) | (Required\*) The environment that this prediction/actual is for (Production, Training, Validation) \*(as of v.5.X.X). |
| `model_version` | `str` | (Optional) Used to group together a subset of predictions and actuals for a given `model_id`. Defaults to `no_version`. |
| `prediction_id` | `str` | (Optional) A unique string to identify a prediction event.
**Important:** This value matches a prediction to an actual label or SHAP feature importance in the Arize platform. If not provided, Arize may create a random prediction ID server-side. |
| `prediction_timestamp` | `int` | (Optional) Unix epoch time in seconds. If `None`, defaults to the current timestamp. **Important:** Future and Historical predictions are supported up to 1 year from the current wall clock time. |
| `prediction_label` | One of `str`, `bool`, `int`, `float`, `Tuple[str,float]` | (Optional) The predicted value for a given model input. *Ingest ranking predictions as a [ranking object](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-log/ranking).* |
| `actual_label` | One of `str`, `bool`, `int`, `float`, `Tuple[str,float]` | (Optional) The actual/ground truth value for a given model input. **Important:** Matched to the prediction with the same `prediction_id`. *Ingest ranking actuals as a [ranking object](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-log/ranking).* |
| `features` | `[dict]` | (Optional) **Dictionary** containing human-readable and debuggable model features. Keys must be `str`; values must be one of `str`, `bool`, `float`, `int`, `list of string`, or `TypedValue`. |
| `embedding_features` | \[[dict`](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-log/embedding)] | (Optional) **Dictionary** containing human-readable embedding features. Keys must be `str`; values must be `Embedding` objects. |
| `shap_values` | `[dict]` | (Optional) **Dictionary** containing feature keys and SHAP importance values. Keys must be `str`; values must be `float`. |
| `tags` | `[dict]` | (Optional) **Dictionary** containing metadata added to a prediction ID. Keys must be `str`; values must be one of `str`, `bool`, `float`, `int`, or `TypedValue`. |
| `batch_id` | `str` | (Optional) Only applicable to Validation datasets. Distinguishes different batches under the same `model_id` and `model_version`. |
| `prompt` | [Embedding](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-log/embedding) | (Optional) Embedding object containing the vector (required) and raw text (optional) for the input text your `GENERATIVE_LLM` model acts on. |
| `response` | [Embedding](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-log/embedding) | (Optional) Embedding object containing the vector (required) and raw text (optional) for the text generated by your `GENERATIVE_LLM` model. |
| `prompt_template` | `str` | (Optional) Template used to construct the prompt. Can include variables using double braces, e.g., `Given the context {{context}}, answer the following question {{user_question}}`. |
| `prompt_template_version` | `str` | (Optional) The version of the template used. |
| `llm_model_name` | `str` | (Optional) The name of the LLM used, e.g., `gpt-4`. |
| `llm_params` | `str` | (Optional) Invocation hyperparameters passed to the LLM, e.g.,
`{ "temperature": 0.7,`
`"stop":[".","?"],`
`"frequency_penalty":"0.2"`
`}` |
| `llm_run_metadata` | [LLMRunMetadata](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-log/llmrunmetadata) | (Optional) Run metadata for LLM calls, e.g.,
`LLMRunMetadata(total_token_count=400, prompt_token_count=300, response_token_count=100, response_latency_ms=2000)` |
### Code Example
```bash theme={null}
future = arize_client.log(
prediction_id=record["prediction_id"],
features={
"f1": 7,
"f2": TypedValue(value=5, type=ArizeTypes.FLOAT),
},
prediction_label=record["predicted_label"],
actual_label=record["actual_label"],
model_id="binary-classification-metrics-only-single-record-ingestion-tutorial",
model_type=ModelTypes.BINARY_CLASSIFICATION,
model_version="1.0.0",
environment=Environments.PRODUCTION
)
result = future.result()
```