Queries
How the ontology is used to make AI-generated queries
TextQL uses an Ontology to generate AI-assisted database queries. This page explores the two main types of queries—Object Queries and Metric Queries.
Sample Ontology: Objects represent key concepts within a business' datawarehouse. Queries are used to extract data from the ontology.
Object vs. Metric Queries
TextQL supports two main categories of queries:
When you begin a chat or analysis, you must start with a query (even if “Ana” creates it automatically). It’s crucial to recognize whether you need an Object Query or a Metric Query for your question.
Object Queries
Object queries are designed to surface all information about a specific “object” (noun) in your business. Every row of the resulting table will correspond to a unique instance of that object.
Examples of questions that fit Object Queries
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Which store has the most revenue?
(You want a table where each row represents a unique store, possibly including a revenue metric.) -
How many orders were shipped to Canada?
(You want a table where each row represents a unique order. If you have location data, you might also see attributes like “country” attached to each row.)
Key Characteristics:
- The query returns rows corresponding to a single object—for example, “Store” or “Order.”
- Attributes from related objects can be joined in automatically. For example, if every store has a regional manager, then the name of the regional manager can be included when the store object is queried.
- Aggregated metrics from “downstream” objects can be included. (If each store has multiple orders, total profit from the “Order” object can be aggregated by store.)
Because object queries focus on one entity, they do not automatically break down metrics by every possible dimension. If you need a fully broken-down metric (e.g., total profit by quarter, region, etc.), you will want to use a Metric Query.
Example: Querying the Location object returns a table where each row corresponds to a location, and collumns refer to the attributes of that location.
Metric Queries
Metric queries are designed to surface quantitative measures (metrics) broken down by dimensions. The result table has each row corresponding to a specific slice of a metric along a dimension or category.
Key Characteristics:
- Direct focus on numerical measurements, such as revenue, profit, or quantity.
- Rows represent slices or groups of these metrics across defined dimensions (e.g., time, geography, product category).
- Ideal for pivot-table-style breakdowns (e.g., total sales per city per month).
Example: Querying the Total Profit metric returns a table where each row coresponds to a partocular aggregation of profit, and where columns describe the dimensions that the metic can be further aggregated on.
Technical Deep Dive
The below section is rather intricate and technical, and deals mainly with how TextQL generates queries and not how to work with them. Feel free to skip it.
Structure of an Ontology Query
In TextQL, each ontology query is composed of up to five major parts. Regardless of whether it’s an Object Query or a Metric Query, these elements guide how data is retrieved and displayed:
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Core Fact Object
- Definition: The primary object (or noun) around which the query is centered.
- Purpose: Determines the main data source and how other entities join.
- Example: “Sale” as the core fact object in a sales analysis.
-
Metrics
- Definition: The quantifiable measurements (e.g., sum, count, average) you want to analyze.
- Purpose: Provide numerical insights, often aggregated.
- Example: “Total Sales” or “Average Order Value.”
-
Dimensions
- Definition: Attributes or fields that categorize your metrics.
- Purpose: Break down metrics into slices (e.g., time, region, product category).
- Example: “Store Location,” “Order Date,” or “Product Type.”
-
Filters
- Definition: Criteria that include or exclude data from the query.
- Purpose: Narrow down your dataset to the most relevant subset.
- Example: Sales in the last quarter or orders above a certain dollar amount.
-
Orders
- Definition: The sorting or ordering applied to the results.
- Purpose: Make it easier to see trends, top performers, or chronological sequences.
- Example: Sorting by descending “Total Sales” to identify best-selling products first.
Object Queries
Coming Soon: A thorough exploration of the search algorithm for object queries, including how TextQL:
- Identifies the core fact object
- Joins relevant attributes
- Handles downstream metrics
- Optimizes performance across large datasets
Stay tuned for more details.
Metric Queries
Coming Soon: A deep dive into the technical underpinnings of metric queries, including:
- Defining and retrieving metrics from multiple objects
- Dimension-based slicing
- Aggregation functions and performance considerations
- Intelligent grouping and pivoting
Stay tuned for more details.
Next Steps
You should now have a basic understanding of how object queries differ from metric queries and the fundamental structure of any ontology query. In subsequent sections, we will dive deeper into the technical details and best practices for setting up your own business ontology in TextQL.