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Proper documentation of tables and columns provides the foundational context required for formal semantic modeling. This initial step is essential for translating technical database structures into familiar business concepts and is critical for data governance, user understanding, and effective analytics. This guide will show you how to do it.

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Document Tables

For each table in your data model, you can configure the following fields:
FieldDescriptionExample
Display NameA business-friendly alias that replaces technical or system-generated table names. You can assign unique display names to the same table across different domains. If left blank, the system defaults to the technical table name for SQL generation.The table name “contact” can be displayed as “B2B Contact Details”, clarifying the type of contact data represented in the table.
DescriptionIn this field, you should provide a clear explanation of what the table represents, focusing on a concise, plain-English description of each row’s purpose and content.This table stores detailed records of business accounts that have monthly revenue exceeding $10,000. Each row represents a single account and includes fields such as account ID, account name, industry classification, account creation date, current status (active, inactive, suspended), monthly revenue amount, and primary contact information.
Always specify the granularity of each row in your table description. This helps analysts understand the level of detail in the data and prevents misinterpretation during analysis.
To Document tables, go to Domains > Select a Domain > Data Sources > Tables. The table is listed with the Display name and Description highlighted

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Document Columns

To document columns, click on the table’s name. A new page will open, displaying a list of all columns in the table. You can configure the following fields:
FieldDescription
Display NameUser-friendly name shown in the interface replaces technical or system-generated table names. You can assign unique display names to the same table across different domains. If left blank, the system defaults to the technical table name for SQL generation.
DescriptionClear explanation of what the column represents in business terms.
Display FormatA dropdown menu used to specify how data is presented to users. You can choose from options such as Date, Fiscal Year, Currency, Percentage, or Time to ensure the AI renders results in the most readable format.
The column is listed with the Display name and the Descrption highlighted

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Advanced Column Options

If you toggle the Show advanced options switch on, the following additional configuration fields will appear, allowing for more granular control over your data model:
FieldDescription
Primary KeyIdentifies if the column is a unique identifier for the rows in the table.
Default OrderSets the default sorting direction (Ascending or Descending) for the column.
Allowed valuesDefines a specific list of acceptable values for the column.
Order allowed valuesEnables or disables a specific custom sorting order for the defined allowed values.
User identifierMarks the column as containing user-specific identifying information such as email or username.
Enable LookupsToggles whether this column can be used for data lookup operations.
A table view of database columns showing advanced configuration options, such as Primary Key and Allowed Values, enabled by the "Show advanced options" toggle.

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Set up RAG for Textual Columns

Retrieval-Augmented Generation (RAG) for Textual Columns is a feature that enhances the AI’s ability to understand and search through the data within a specific table. By configuring RAG for a table, you enhance the AI’s ability to search through data by selecting specific columns for different functions:
  • Text content for search: You can define which columns contain rich text that should be searched over.
  • Filters for queries: You can select columns with structured data, such as IDs and dates, which can be used to narrow down search results.
  • Primary Keys: You can pick columns that uniquely identify each row, which aids in data retrieval.
To configure RAG for a table, follow these steps:
  1. Navigate to Domains > Select your preferred domain > Data Sources > Tables and locate the table for which you want to configure RAG columns.
  2. Click on the three vertical dots icon and then on RAG Settings.
RAG Configuration Columns 01 Pn
  1. In the RAG Configuration for Textual Columns window that opens, complete the following:
    • In the Text content columns for search field, select the columns that contain rich text content that will be searched over.
    • In the Filter columns for queries field, select columns with structured data (like IDs or dates) that can be used to narrow down search results.
    • In the Primary key field, choose one or more columns that uniquely identify each row in the table.
    • In the Description of search columns text box, write a brief description of what the combination of the search columns represents. This helps the AI better understand the context of your data.
    • (Optional) Click Advanced Options to specify columns for citation titles and URLs in the AI’s answers.
  2. Click Save to apply your settings. A notification will appear confirming that the configuration is updated and the data sync has been triggered.
RAG Configuration Columns 01 Pn

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Next Steps

Advanced Data Modeling

Define relationships and context in your data to enable more powerful analysis.

Provide Feedback to the System

Learn how to rate answers and provide corrections to improve the system’s accuracy.

Configure Domains

Manage and customize your data domains to refine context and improve query results.