logger#

Use this to log evaluations or spans to Arize in bulk. Read our quickstart guide for logging evaluations to Arize.

To use in your code, import the following:

from arize.pandas.logger import Client

client#

class Client(api_key=None, space_id=None, space_key=None, uri='https://api.arize.com/v1', additional_headers=None, request_verify=True)#

Bases: object

Arize API Client to log predictions and actuals to the Arize platform from pandas.DataFrames

log_evaluations(dataframe, model_id, model_version=None, validate=True, path=None, timeout=None, verbose=False)#

Logs a pandas dataframe containing LLM evaluations data to Arize via a POST request. The dataframe must contain a column context.span_id such that Arize can assign each evaluation to its respective span. Returns a Response object from the Requests HTTP library to ensure successful delivery of records.

Parameters:

  • dataframe (pd.DataFrame) – A dataframe containing LLM evaluations data.

  • model_id (str) – A unique name to identify your model in the Arize platform. It should match the model_id of the spans sent previously, to which evaluations will be assigned.

  • model_version (str, optional) – Used to group a subset of traces a given model_id to compare and track changes. It should match the model_id of the spans sent previously, to which evaluations will be assigned. Defaults to None.

  • validate (bool, optional) – When set to True, validation is run before sending data. Defaults to True.

  • path (str, optional) – Temporary directory/file to store the serialized data in binary before sending to Arize.

  • timeout (float, optional) – You can stop waiting for a response after a given number of seconds with the timeout parameter. Defaults to None.

  • verbose – (bool, optional) = When set to true, info messages are printed. Defaults to False.

Returns:

A Response object from the requests library.

log_spans(dataframe, model_id, model_version=None, evals_dataframe=None, datetime_format=DEFAULT_DATETIME_FMT, validate=True, path=None, timeout=None, verbose=False)#

Logs a pandas dataframe containing LLM tracing data to Arize via a POST request. Returns a Response object from the Requests HTTP library to ensure successful delivery of records.

Parameters:

  • dataframe (pd.DataFrame) – The dataframe containing the LLM traces.

  • model_id (str) – A unique name to identify your model in the Arize platform.

  • model_version (str, optional) – Used to group a subset of traces a given model_id to compare and track changes. Defaults to None.

  • evals_dataframe (pd.DataFrame, optional) – A dataframe containing LLM evaluations data. The evaluations are joined to their corresponding spans via a left outer join, i.e., using only context.span_id from the spans dataframe. Defaults to None.

  • datetime_format (str) – format for the timestamp captured in the LLM traces. Defaults to “%Y-%m-%dT%H:%M:%S.%f+00:00”.

  • validate (bool, optional) – When set to True, validation is run before sending data. Defaults to True.

  • path (str, optional) – Temporary directory/file to store the serialized data in binary before sending to Arize.

  • timeout (float, optional) – You can stop waiting for a response after a given number of seconds with the timeout parameter. Defaults to None.

  • verbose – (bool, optional) = When set to true, info messages are printed. Defaults to False.

Returns:

Response object