Python client for ikigailabs

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ikigai

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Table of Contents

Ikigai Platform Overview

The Ikigai Python library provides access to Ikigai's Platform API from applications written in the Python language.

Ikigai enables you to build artificial intelligence apps, or AI apps, that support business intelligence, machine learning and operational actions.

Apps are the basic organizational units in Ikigai. Apps are much like folders and they contain all the components that work together to produce your desired output. An app includes Connectors, Datasets, Flows, Dashboards, and Models. You begin by creating an app, and then connecting to data. The data can exist in a variety of forms, such as records in a database, information in a spreadsheet or data in an application. To connect to different sources of data, you use connectors.

Once you can access data, you create flows, which are pipelines to process and transform the data. In each flow you can add pre-built building-blocks that perform operations on your data. These building-blocks are called facets. With flows, you can store rules which restrict who can access data, define how data should appear in a standardized form and transform data so it’s easier to analyze. Flows are reusable, which means you or others can define them once and apply them to other apps.

Getting an API Key

The library needs to be configured with your account's API key which is available by logging into the Ikigai platform. To generate your API key follow the steps below.

  1. Once logged in, go to your account, under Profile > Account. 

  2. Select the Keys option.

  3. Click Generate API Key to generate a unique API key.

  4. Click the Eye icon to reveal the API key. Save this key in a secure place and do not share it with anyone else. You will need this API key to use Ikigai's Python client library in the next sections.

Requirements

You should have the latest stable version of Python installed in your environment (~3.12) to use Ikigai's Python client library. Ikigai will support Python version 3.9 until its EOL (October 31, 2025).

Installation

Use the Python Package Index (PyPI) to install the Ikigai client library with the following command:

pip install ikigai

Creating an Ikigai Client

In this section, you create the Ikigai client. The code snippet first imports the Ikigai library. Then, a new Ikigai() object is created. This object requires your user email and the API key that you generated in the previous section.

from ikigai import Ikigai

ikigai = Ikigai(user_email="bob@example.com", api_key="my-ikigai-api-key")

Examples

Once you have initiated the Ikigai client, you have access to all Ikigai components that are exposed by the Python library. The sections below provide examples for each component and common actions you might perform with each one.

Apps

Apps are the basic organizational units in Ikigai. Apps contain all the components that will work together to produce your desired output.

Listing All Apps

The code snippet below gets all the apps that are accessible by your account and stores them in the apps variable. Then, it uses a loop to print each app.

apps = ikigai.apps()       # Get all apps accessible by you
for app in apps.values():  # Print each app
    print(app)

The output resembles the following example:

Start Here (Tutorial)
Example Project
proj-1234

Showing Details of an App

The code snippet below gets all the Apps that your account can access and stores them in the apps variable. Then, it gets the app named my app and stores it in the app variable. Finally, it prints details about the app, using the describe() method.

apps = ikigai.apps()
app = apps["my app"]       # Get the app named "my app"
print(app.describe())      # Print the details of my app

The output resembles the following example:

{'app': {'app_id': '12345678abcdef',
  'name': 'Start Here (Tutorial)',
  'owner': 'bob@example.com',
  'description': '',
  'created_at': datetime.datetime(2024, 12, 13, 20, 0, 52, tzinfo=TzInfo(UTC)),
  'modified_at': datetime.datetime(2024, 12, 13, 20, 0, 52, tzinfo=TzInfo(UTC)),
  'last_used_at': datetime.datetime(2025, 1, 23, 18, 30, 7, tzinfo=TzInfo(UTC))},
 'components': {'charts': [{'chart_id': '88888888fffffff',
    'name': 'Dataset: junk',
    'project_id': 'abcdefg123456',
    'dataset_id': '4444444bbbbbbb',
    'superset_chart_id': '40929',
    'data_types': {}},
   {'chart_id': '9999999ggggggg',
    'name': 'Dataset: New Dataset',
    'project_id': 'abcdefg123456',
    'dataset_id': '987654zyxwvu,
    'superset_chart_id': '40932',
    'data_types': {}},
...
  'dataset_directories': [{'directory_id': '33333333iiiiiiii',
    'name': '[OUTPUT]',
    'type': 'DATASET',
    'project_id': 'abcdefg123456',
    'parent_id': '',
    'size': '0'},
   {'directory_id': '7777777ggggggg',
    'name': '[INPUT]',
    'type': 'DATASET',
    'project_id': 'abcdefg123456',
    'parent_id': '',
    'size': '0'}],
  'database_directories': [],
  'pipeline_directories': [],
  'model_directories': [],
  'external_resource_directories': []}}

Datasets

Datasets are any data files stored in the Ikigai platform. You can upload your files to Ikigai to create a dataset. Ikigai supports various file types such as CSV, and Pandas DataFrame.

Finding a Dataset from an App

The code snippet below gets all the apps that your account can access and stores them in the apps variable. Then, it gets the app named my app and stores it in the app variable. You can now access all datasets that are associated with my-app. The code stores all the datasets in the datasets variable. Next, it access the dataset named my-dataset and stores it in the dataset variable and prints its contents.

apps = ikigai.apps()
app = apps["my app"]              # Get the app named "my app"
datasets = app.datasets()         # Get all datasets in my app
dataset = datasets["my dataset"]  # Get dataset named "my dataset"
print(dataset)

The output resembles the following example:

Dataset(
    app_id='12345678abcdef',
    dataset_id='4444444bbbbbbb',
    name='Dataset: New Dataset',
    filename='example.csv',
    file_extension='csv',
    data_types={
        'Channel/Location': ColumnDataType(
            data_type=<DataType.CATEGORICAL: 'CATEGORICAL'>,
            data_formats={}
        ),
        'Product (name/description)': ColumnDataType(
            data_type=<DataType.TEXT: 'TEXT'>,
            data_formats={}
        ),
        'Quantity': ColumnDataType(
            data_type=<DataType.NUMERIC: 'NUMERIC'>,
            data_formats={}
        ),
        'SKU/Unique Item ID': ColumnDataType(
            data_type=<DataType.TEXT: 'TEXT'>,
            data_formats={}
        )
    },
    size=311,
    created_at=datetime.datetime(2024, 1, 1, 20, 0, 55, tzinfo=TzInfo(UTC)),
    modified_at=datetime.datetime(2024, 1, 1, 22, 0, 55, tzinfo=TzInfo(UTC))
)

Showing Details of a Dataset

The example snippet shows you how to display details related to a dataset. First, get all datasets stored in a particular app. The example stores all datasets in the datasets variable. Next, store the dataset in a variable. The example stores the [INPUT] Amazon dataset in the datasaet variable. Next, use the describe() method to view a dictionary containing all the dataset's details.

datasets = app.datasets()             # Get all datasets in the app
dataset = datasets["[INPUT] Amazon"]  # Get dataset named "[INPUT] Amazon"
dataset.describe()

The output resembles the following example:

{
    'dataset': {
        'dataset_id': '4444444bbbbbbb',
        'name': 'Start Here (Tutorial)',
        'project_id': 'abcdefg123456',
        'filename': 'example.csv',
        'data_types': {
            'Channel/Location': {
                'data_type': 'CATEGORICAL',
                'data_formats': {}
            },
            'Product (name/description)': {
                'data_type': 'TEXT',
                'data_formats': {}
            },
            'Quantity': {
                'data_type': 'NUMERIC',
                'data_formats': {}
            },
            'SKU/Unique Item ID': {
                'data_type': 'TEXT',
                'data_formats': {}
            }
        },
        'directory': {
            'directory_id': '33333333iiiiiiii',
            'name': '',
            'type': 'DATASET',
            'project_id': ''
        },
        'upload_id': ''
    }
}

Downloading Your Existing Dataset

The example snippet shows you how to download an existing dataset. First, get all datasets stored in a particular app. The example stores all datasets in the datasets variable. Next, store the dataset in a variable. The example stores the [INPUT] Amazon dataset in the datasaet variable. Then, download the dataset to a Pandas DataFrame. You can pass an argument to the .head() method to designate how many rows of the dataset to display (i.e. df.head(10)). By default, the method displays the first 5 rows of the dataset.

datasets = app.datasets()             # Get all datasets in the app
dataset = datasets["[INPUT] Amazon"]  # Get dataset named "[INPUT] Amazon"
df = dataset.df()                     # Download the dataset as a a pandas dataframe

df.head()

The output resembles the following example:

Product (name/description) SKU/Unique Item ID Channel/Location Quantity
Chocolate Chip Cookie Chocolate_C123_Am Amazon 166
Snickerdoodle Cookie Snickerdoodle_C123_Am Amazon 428
Ginger Cookie Ginger_C123_Am Amazon 271
Sugar Cookie Sugar_C123_Am Amazon 421
Double Chocolate Cookie Double_C123_Am Amazon 342

Creating a New Dataset

The example snippet shows you how to create a new dataset using a Panda's DataFrame. First, create a new DataFrame object. The example stores the new DataFrame object in the df variable. Next, build a new dataset named New Dataset using the data stored in the df variable. Calling the new_dataset variable returns details about the dataset.

df = pd.Dataframe({"Name": ["Alice", "Bob"], "Age": [25, 30]})
new_dataset = app.dataset.new("New Dataset").df(df).build()  # Build a new dataset named "New Dataset" with data df

new_dataset

The output resembles the following example:

Dataset(
    app_id='12345678abcdef',
    dataset_id='3232323yyyyyyy',
    name='New Dataset',
    filename='new-example.csv',
    file_extension='csv',
    data_types={
        'Channel/Location': ColumnDataType(
            data_type=<DataType.CATEGORICAL: 'CATEGORICAL'>,
            data_formats={}
        ),
        'Product (name/description)': ColumnDataType(
            data_type=<DataType.TEXT: 'TEXT'>,
            data_formats={}
        ),
        'Quantity': ColumnDataType(
            data_type=<DataType.NUMERIC: 'NUMERIC'>,
            data_formats={'numeric_format': 'INTEGER'}
        ),
        'SKU/Unique Item ID': ColumnDataType(
            data_type=<DataType.TEXT: 'TEXT'>,
            data_formats={}
        )
    },
    size=305,
    created_at=datetime.datetime(2025, 1, 23, 18, 22, 2, tzinfo=TzInfo(UTC)),
    modified_at=datetime.datetime(2025, 1, 23, 18, 22, 9, tzinfo=TzInfo(UTC))
)

Updating a Dataset

The example snippet shows you how to update an existing dataset. First, get all datasets stored in a particular app. The example stores all datasets in the datasets variable. Next, store the dataset in a variable. The example stores the [INPUT] Amazon dataset in the datasaet variable. Then, download the dataset as a Pandas DataFrame. The code stores the DataFrame in the df variable. Now, you can update the DataFrame using the .columns() method. The example uses the .columns() method to drop the last column in the DataFrame. It stores the updated DataFrame in a new variable named new_dataset. Finally, update the dataset with the new data using the .edit_data() method. To complete this pass in the DateFrame df_updated as an argument of the .edit_data() method. Display the data in the new dataset using the .head() method.

datasets = app.datasets()             # Get all datasets in Start the app
dataset = datasets["[INPUT] Amazon"]  # Get dataset named "[INPUT] Amazon"
df = dataset.df()                     # Download the dataset as a a pandas dataframe
df_updated = df[df.columns[:-1]]      # New dataframe (by dropping last column)
new_dataset.edit_data(df_updated)     # Update the dataset

new_dataset.df().head()

The output resembles the following example:

Product (name/description) SKU/Unique Item ID Channel/Location
Chocolate Chip Cookie Chocolate_C123_Am Amazon
Snickerdoodle Cookie Snickerdoodle_C123_Am Amazon
Ginger Cookie Ginger_C123_Am Amazon
Sugar Cookie Sugar_C123_Am Amazon
Double Chocolate Cookie Double_C123_Am Amazon

Flows

Flows are data automations that you can build by connecting elements known as facets. Flows ingest data, transform data, and then output data. Flows can do anything from data cleaning to forecasting, scenario analysis, optimizing decisions, creating alerts, and more. This is where all of our data cleaning, prepping, and processing happen.

Finding a Flow from an App

The example snippet below shows you how to find a specific flow from an existing app. First, access the app. The example gets the Start Here (Tutorial) app and stores it in a variable named app. Next, get all the flows that belong to the app. The example uses the flows() method to get all the app's flows and stores it in the flows variable. Now, retrieve the specific flow. The example gets the flow named new flow and stores it in the flow variable. Calling the flow variable prints out details about the flow.

app = apps["Start Here (Tutorial)"]     # Get the app named "Start Here (Tutorial)"
flows = app.flows()                     # Get all flows in the app
flow = flows["new flow"]                # Get flow named "new flow"

flow

The output resembles the following example:

Flow(
    app_id='12345678abcdef',
    flow_id='6666666hhhhhhh',
    name='new flow',
    created_at=datetime.datetime(2025, 1, 1, 10, 0, 30, tzinfo=TzInfo(UTC)),
    modified_at=datetime.datetime(2025, 1, 1, 11, 0, 30, tzinfo=TzInfo(UTC))
)

Running a Flow

The example snippet shows you how to run a flow that is used by your app. First, get all the flows that belong to the app. The example uses the flows() method to retrieve all the flows that are stored in the app variable and stores them in a variable named flows. The example uses the flows() method to get all the flows stored in the app variable and stores them in a variable named flows. Now, retrieve the specific flow. The example gets the flow named new flow and stores it in the flow variable. Finally, run the flow by calling the .run() method.

flows = app.flows()        # Get all flows in the app
flow = flows["new flow"]   # Get flow named "new flow"

flow.run()                 # Run the flow

When the run is successful, the output resembles the following example:

RunLog(
    log_id='4545454lllllll',
    status=SUCCESS,
    user='bob@example.com',
    erroneous_facet_id=None,
    data='',
    timestamp=datetime.datetime(2025, 1, 1, 11, 0, 5, tzinfo=TzInfo(UTC))
)

Getting the Status of a Flow

The example snippet shows you how to view the status of an existing flow. First, get all the flows that belong to the app. The example uses the flows() method to get all the flows stored in the app variable and stores them in a variable named flows. Now, retrieve the specific flow. The example gets the flow named new flow and stores it in the flow variable. To view the flow's status use the status() method.

flows = app.flows()         # Get all flows in the app
flow = flows["new flow"]    # Get flow named "new flow"

flow.status()               # Get the status of the flow (IDLE: currently the flow is not running)

When the flow is NOT running, you should see a similar output:

FlowStatusReport(
    status=IDLE,
    progress=None,
    message=''
)

License

  • ikigai is distributed under the terms of the MIT license.

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