Databases

1 Introduction

In addition to unstructured Knowledge (documents, webpages, manual inputs), our platform also supports Datasets, which are designed for structured data. A Dataset functions like a lightweight database that enables you to organize information into tables with custom fields, making it easy to manage and query data consistently across your workspace.

1.1 What is a Dataset?

A Dataset is a structured data repository, similar to creating a database. Inside a dataset, you can create one or more tables, each with a customizable schema (fields/columns). Datasets are ideal for storing information that is naturally tabular—such as customers, products, tickets, SEO tasks, or any other records you want to manage with defined fields.

1.2 Why Use Datasets?

Datasets provide several practical benefits:

• Structured Organization: Data is stored in tables with defined fields, making it consistent and easier to maintain.

• Custom Schema: You define exactly which fields you need (e.g., text, numbers, tags, etc.).

• Better Data Management: Update dataset name/description anytime in Settings to keep data ownership and purpose clear.

• Query-Friendly: Table and column IDs are designed for SQL operations, enabling future querying or integration use cases.

2 Create a Dataset

2.1 Database Tab

Navigate to Knowledge Center → Database to view all existing datasets (databases).

From this page, you can create a new dataset or open an existing one.

2.2 Create and Configure Dataset Settings

To create a new dataset:

1. Click Create Database.

2. In the creation dialog:

   • Name: A unique dataset name (e.g., customer_db, seo_db)

   • Description: A short explanation of what the dataset is used for

3. Click Save to create the dataset.

Create Database

Create a structured database for SQL operations and data management.

3 Create Tables Inside a Dataset

3.1 Open the Dataset

Once created, the dataset will appear in the database list. Click the dataset card to enter it (e.g., test_Database).

3.2 Create a Table

Inside the dataset, go to Tables and click Create Table.

Create Table

Create a structured database table for SQL operations.

3.3 Define Table Schema (Custom Fields)

When creating a table, you can fully customize its structure:

• Table Name: Used internally (recommended: lowercase + underscores, e.g., users, orders, seo_tasks)

• Display Title: Shown in UI (e.g., Users, Orders, SEO Tasks)

• Fields (Columns):

  • Column ID: Used in SQL queries (recommended: snake_case, e.g., customer_name)

  • Field Name: Display name in UI (e.g., Customer Name)

  • Field type (e.g., Single Line Text)

  • Index option (e.g., No Index)

The system may auto-generate common fields such as Id (Primary Key), CreatedAt, UpdatedAt.

4 Dataset Settings (Rename / Update Description)

Each dataset has a Settings page where you can update:

• Name

• Description

After editing, click Save.

3.4 Table View (Data & Fields)

After creating a table, you can manage it through two main tabs: Data and Fields.

This makes it easy to separate record operations (what’s inside the table) from schema design (how the table is structured).

3.4.1 Data Tab (View & Manage Records)

The Data tab displays the records stored in your table in a spreadsheet-like view.

Here you can quickly review values, scan key identifiers, and validate that your data is being written correctly.

Common system fields (e.g., Id (PK), CreatedAt, UpdatedAt) are shown alongside your custom fields.

3.4.2 Fields Tab (Define Table Schema)

The Fields tab is where you define and maintain the structure of the table:

• View all custom fields

• Check each field’s Type (e.g., Text)

• Configure Index settings (if available)

• Edit or delete fields using the action icons

• Add new fields via New Field

This is the recommended place to evolve your table schema over time, especially when you need to support new data attributes.