Check out the docs for the latest features and information.
Although this package is used for development work with and within the Arize platform, it is not an Arize product. It is a open source project developed and maintained by an Arize Engineer. Feel free to add issues or reach out for help in the Arize community Slack channel.
Arize Toolkit is a set of tools packaged as a Python client that lets you easily interact with Arize AI APIs. Here's a quick overview of the main features in the current release:
- Access and manage models
- Retrieve performance metrics over a time period
- Retrieve inference volume over a time period
- Create, copy, and manage custom metrics
- Create, copy, and manage monitors and alerting
- Work with LLM features like prompts and annotations
- Import data from cloud storage (S3, GCS, Azure) and databases (BigQuery, Snowflake, Databricks)
- Create, update, and delete data import jobs with full lifecycle management
pip install arize_toolkitThe Client class is the entrypoint for interacting with the toolkit. It provides maintains the connection information for making requests to the Arize APIs, and offers a wide range of operations for interacting with models, monitors, dashboards, and more.
To create a client, you need to provide your Arize API key. Use this reference to get your API key from the Arize UI.
You will also need to provide an organization name and space name. To give some context, models are scoped to a space, and the space is scoped to an organization. These can be found by navigating to the Arize UI and looking at the upper left path in the Projects & Models page. They will be in the format organization/space.
For the example below, the organization is Demo Models and the space is Demo Model Manager.
For SaaS users, the default API endpoint is always going to be https://api.arize.com.
If you are using an on prem deployment of Arize, you will need to provide the api_url parameter.
This parameters should just be the base url of your Arize instance.