> ## Documentation Index
> Fetch the complete documentation index at: https://kumo.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

This guide is designed to help you dive deeper into the Predictive Query Language (PQL), a SQL-like syntax that powers Kumo's predictive modeling tasks. This reference will provide you with a more granular look at the commands and operators available in PQL.

The reference is organized into three main sections:

* **Primary Commands**

  This section introduces the core building blocks of PQL, including commands such as `ASSUMING`, `RANK`, `FOR EACH`, `PREDICT`, and `WHERE`. These commands allow you to define the data context, specify your prediction targets, and structure your query for model training.
* **Aggregation Operators**

  Here, you'll find operators that perform aggregate computations on your data. Functions like `AVG`, `COUNT`, `COUNT_DISTINCT`, `FIRST`, `LAST`, `LIST_DISTINCT`, `MAX`, `MIN`, and `SUM` help you create summary statistics within your predictive queries.
* **Boolean Operators**

  This section covers the logical and comparison operators used to filter and refine your queries. You'll learn about operators such as `AND`, `OR`, `NOT`, along with pattern matching functions like `CONTAINS`, `LIKE`, `NOT LIKE`, `STARTS WITH`, and `ENDS WITH`. These tools are essential for crafting complex conditions that accurately target your data.

Happy querying!