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Documentation Index

Fetch the complete documentation index at: https://docs.ideaboxai.com/llms.txt

Use this file to discover all available pages before exploring further.

After creating a structured knowledge base, you connect it to a database and explore the imported schema. This guide walks you through entering connection credentials, browsing tables, and enriching metadata.

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Connect your database

Enter the connection credentials for your database.
FieldDescription
HostDatabase server address.
PortConnection port, for example 5432 for PostgreSQL.
Database NameThe specific database to connect.
UsernameAuthentication username.
PasswordAuthentication password.
SSL ModeSecurity settings for the connection.
Connect External Data Source dialog showing database type selector and credential fields for Host, Port, Database Name, Username, Password, and SSL Mode. Database credential entry form with fields for Host, Port, Database Name, Username, Password, and SSL Mode, along with connection test and submit buttons. After a successful connection, IdeaBoxAI imports the database schema automatically.

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Explore the Datasets view

Once connected, the Datasets view displays a schema visualization of your database.
  • Data Sets sidebar: Lists all imported database tables.
  • Table relationships: Shows joins and foreign key relationships between tables.
  • AI enrichment: Options to enhance metadata with AI, add semantic definitions, and define relationships.
Datasets view showing database tables in the sidebar, table relationships in the main panel, and options to enhance with AI, add semantics, and define relationships. From this view you can:
  • Browse all database tables and their columns.
  • Define joins and relationships between tables.
  • Enhance schema metadata using AI assistance.
  • Document business logic for columns and tables.
Best practices
  • Validate relationships against source database constraints.
  • Avoid circular or ambiguous joins.
  • Document semantic definitions clearly so agents can reason about your data.
The result is a semantically enriched data model ready for analytics and AI queries through cubes.