Quick Start

Explore this quickstart notebook for step-by-step explanations.

Install the SDK

Install the python SDK

pip install kumoai

Get an API key

You need an API key to make calls to KumoRFM. You can generate an API key for free at https://kumorfm.ai. Currently the daily query limit is 1000/day.

Initialize a client

Use the generated API key to intialize a kumo client:

import kumoai.experimental.rfm as rfm, os

os.environ["KUMO_API_KEY"] = "ENTER_YOUR_API_KEY_HERE"

rfm.init()

Import your data

We will use an E-Commerce dataset for this example:

import pandas as pd

dataset_url = "s3://kumo-sdk-public/rfm-datasets/online-shopping"

users_df = pd.read_parquet(f"{dataset_url}/users.parquet")
items_df = pd.read_parquet(f"{dataset_url}/items.parquet")
orders_df = pd.read_parquet(f"{dataset_url}/orders.parquet")

Create a graph

Auto infer links between tables to form a graph

graph = rfm.LocalGraph.from_data({
    "users": users_df,
    "items": items_df,
    "orders": orders_df,
})

# Inspect the graph - requires graphviz to be installed
graph.visualize()

Make a prediction

Write a predictive query to generate instant predictions:

model = rfm.KumoRFM(graph)

# Forecast 30-day product demand
query1 = "PREDICT SUM(orders.price, 0, 30, days) FOR items.item_id=1"
result1 = model.predict(query1)
display(result1)

# Predict customer churn
query2 = "PREDICT COUNT(orders.*, 0, 90, days)=0 FOR users.user_id IN (42, 123)"
result2 = model.predict(query2)
display(result2)

# Item recommendation
query3 = "PREDICT LIST_DISTINCT(orders.item_id, 0, 30, days) RANK TOP 10 FOR users.user_id=123"
result3 = model.predict(query3)
display(result3)

# Missing value imputation
query4 = "PREDICT users.age FOR users.user_id=8"
result4 = model.predict(query4)
display(result4)