> ## 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. # Knowledge base connections > Learn how to connect databases, documents, and cloud warehouses to IdeaBoxAI knowledge bases for AI-powered querying. A knowledge base connection links IdeaBoxAI to your data. Whether that is a live relational database, a cloud data warehouse, or a library of documents, this guide covers all three types with step-by-step configuration. ## Knowledge base types IdeaBoxAI supports three knowledge base types. Each type is optimised for a different data format and query pattern. | KB type | Best for | Supported sources | | --------------------- | ---------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------- | | **Unstructured data** | Documents, PDFs, and text. Optimised for semantic search and AI reasoning. | Device upload, Google Drive, Confluence, Markdown and text. | | **Structured data** | Relational databases and spreadsheets. Supports SQL queries, cubes, analytics, and dashboards. | MySQL, PostgreSQL, Snowflake, ClickHouse, SQL Server, Actian, Zen, Zoho Book Analytics, CSV, and Excel. | ## Set up an unstructured data knowledge base Use this type for documents, playbooks, runbooks, and any content intended for semantic search. In the IdeaBoxAI sidebar, click **Knowledge Bases**. The dashboard shows all existing knowledge bases with name, status, type, creator, and last modified date. Knowledge Bases dashboard showing a searchable table of knowledge bases with Name, Status, Type, Created By, and Last Modified columns. Click **+ Create Knowledge Base**. A type selection modal appears. Select **Unstructured Data** and click **Next**. The Name and Description modal appears. Fill in the following fields: * **Name** (required): Use a clear naming convention, for example "Sales Playbooks 2026" or "RFP Answer Library". * **Description**: Explain what data this knowledge base contains and which personas will use it. * **Tags**: Add domain or team tags, for example "sales", "onboarding", "CRE". Click **Create**. Name and Description modal for creating an Unstructured Knowledge Base with fields for Name, Description, and Tags. The empty Data Source panel appears. Click **+ Add Data** to reveal four options. Add Data dropdown showing four options: Upload From Device, Add Markdown/Text, Upload From Google Drive, and Upload From Confluence. * **Upload From Device**: Drop files or browse to select them. Supports PDF, PNG, JPG, DOCX, TXT, MP3, WAV, OGG, and MP4. Batch upload is supported. * **Add Markdown/Text**: Paste or type content directly into the editor. Useful for SOPs, playbooks, or knowledge articles without a file. * **Upload From Google Drive**: Authenticate with your Google account, browse your Drive folders, and select files or folders to import. * **Upload From Confluence**: Authenticate with your Confluence workspace, browse spaces and pages, and select content to import. The following screenshot shows the Upload From Device modal with its drag-and-drop area. Upload From Device modal with a drag-and-drop area showing supported file formats: PDF, PNG, JPG, DOCX, TXT, MP3, WAV, OGG, and MP4. After upload, each file appears in the data source table with columns for file name, status, file type, and size. The status transitions from **Processing** to **Processed**. Data source table showing an uploaded PDF file with Processing status, file type, and size columns. Wait for all files to show **Processed** before connecting this knowledge base to an agent or automation. If a file shows a failure status, remove it and re-upload. Knowledge Bases dashboard showing a knowledge base with Processed status, confirming the data is ready. Ensure documents are machine-readable text PDFs (not scanned images) for best processing results. Re-index data after any bulk upload. ## Set up a structured data knowledge base Use this type for relational databases, spreadsheets, or any structured data source. Click **+ Create Knowledge Base**. Select **Structured Data**. Enter a name, description, and tags in the Name and Description modal, then click **Create**. Name and Description modal for creating a Structured Knowledge Base with fields for Name, Description, and Tags. The Connect Your External Knowledge Base page appears. Choose from two categories: **Spreadsheet**: CSV, Excel, or Google Sheets *(coming soon)*. **Database Connection**: MySQL, Zen, ClickHouse, SQL Server, PostgreSQL, Actian, Zoho Book Analytics, or Snowflake. Connect Your External Knowledge Base page showing spreadsheet options (CSV, Excel, Google Sheets) and database connection options (MySQL, Zen, ClickHouse, SQL Server, PostgreSQL, Actian, Zoho Book Analytics, Snowflake). If you selected a database connection, the Enter Database Credentials form appears. Fill in the following fields: * **Host** (required): Your database server address or IP. * **Port** (required): Database port (PostgreSQL: 5432, MySQL: 3306, Snowflake: 443). * **Username** (required): A database user with read access. * **Password** (required): The database password. Click **Test** to verify the connection. Enter Database Credentials form for MySQL showing required fields for Host, Port, Username, and Password with a Test button. If you selected CSV, the Upload CSV Files page appears. Click **+ Upload Files** to select CSV files from your device. Wait for all files to show **Processed**. Upload CSV Files page showing a processed CSV file with file name, status, type, and size columns. After a successful connection, IdeaBoxAI imports your database schema. The Datasets view shows: * **Left sidebar**: All database tables (Data Sets). * **Centre panel**: Table relationships, joins, and column details. * **Options**: Enhance with AI, Add Semantics, and Define Relationships. Browse the tables and confirm the schema looks correct. Use **Define Relationships** to specify table joins. Cubes sit on top of your raw tables and define metrics (measures) and groupings (dimensions) for AI queries. Navigate to the **Cubes** tab and click **+ Add Cube** to open the Add New Cube modal. **Option A, AI Generate** (recommended for most cases): Enter a cube name, cube description, natural language query describing the analytics you want, and business context. Click **Generate**. The AI writes the SQL, measures, and dimensions. Add New Cube modal with AI Generate selected, showing fields for Cube Name, Cube Description, Natural Language Query, and Business Context. **Option B, Manual Query** (for advanced users): Enter a cube name, write custom SQL in the SQL Query editor, then define measures and dimensions manually using **+ Add Measure** and **+ Add Dimension**. Add New Cube modal with Manual Query selected, showing a SQL Query editor and sections for adding Measures and Dimensions. Click **Save Cube**. In the cube configuration panel, select measures, dimensions, and filters, then click **Run Query**. Results appear in the Results tab. Also check the **Generated SQL** tab to inspect the query and the **REST API** tab for programmatic access. Validate results against your source data. Navigate to **Agents**, select your agent, open the **Configuration** tab, select this knowledge base from the **Knowledge Base** dropdown, and click **Save**. The agent can now query your structured data using natural language. Always test the generated SQL in the cube query panel before connecting the knowledge base to a live agent. Start with simple cubes and iterate. ## Next steps Browse all available connectors by category. Build analytical models with measures, dimensions, and queryable APIs. Resolve common connection issues for databases and imports.