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get_docsRetrieve documentation on using KumoRFM for predictive analytics on relational data, including graph setup, predictive queries, and explainability.
Instructions
Get documentation on how to use KumoRFM.
KumoRFM is a pre-trained Relational Foundation Model (RFM) that generates training-free predictions on any relational multi-table data by interpreting the data as a (temporal) heterogeneous graph. It can be queried via the Predictive Query Language (PQL).
Internal note: It is NOT related to "Recency, Frequency, Monetary" analysis.
Internally, KumoRFM utilizes in-context learning to transfer patterns from historical examples to new unseen examples. Specifically, it constructs training/in-context subgraphs with known ground-truth labels and relates them to unseen subgraphs.
See the 'kumo://docs/overview' resource for more information.
KumoRFM can discover table-like files (e.g., CSV, Parquet), inspect them, and structure them into a graph via foreign key-primary key relationships. A time column in a table dictates the create time of a row, which is used downstream to receive and order historical interactions and prevent temporal leakage. Each column within a table is assigned a semantic type (numerical, categorical, multi-categorical, ID, text, timestamp, sequence, etc) that denotes the semantic meaning of the column and how it should be processed within the model.
Important: Before creating and updating graphs, read the documentation first at 'kumo://docs/graph-setup'.
After a graph is set up and materialized, KumoRFM can generate
predictions (e.g., missing value imputation, temporal forecasts) and
evaluations by querying the graph via the Predictive Query Language
(PQL), a declarative language to formulate machine learning tasks.
Understanding PQL and how it maps to a machine learning task is
critical to achieve good model predictions. Besides PQL, various other
options exist to tune model output, e.g., optimizing the run_mode of
the model, specifying how subgraphs are formed via num_neighbors, or
adjusting the anchor_time to denote the point in time for when a
prediction should be made.
Important: Before executing or suggesting any predictive queries, read the documentation first at 'kumo://docs/predictive-query'.
KumoRFM can additionally generate explanations for predictions, providing both a global column-level analysis and a local, cell-level attribution view. Together, these views enable comprehensive interpretation.
Important: Before analyzing the explanation output, read the documentation first at 'kumo://docs/explainability'.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| resource_uri | Yes |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |