PostgreSQL connection setup

PostgreSQL connector is available in AI Data Analyst conversational notebooks. In the prompt box, click DB Connector, configure PostgreSQL details, test connection, and save.

Requirements

• PostgreSQL host
• Port (usually 5432)
• Database name
• Schema
• Username
• Password

Connection steps

1. Open MLJAR Studio and create/open an AI Data Analyst notebook.
2. Click DB Connector in the prompt box.
3. Choose PostgreSQL as the connector type.
4. Enter host, port, database, schema, user, and password.
5. Click Test connection.
6. If PostgreSQL Python driver packages are missing, MLJAR Studio asks for install and installs them.
7. When connection test succeeds, click Save connection.
8. MLJAR Studio scans available tables and prepares LLM context.
9. Select which tables should be available to the LLM.
10. Click final Save to confirm table visibility.
11. Verify active connection chip in prompt box and tables in Data Awareness panel on the left sidebar.

SQL block in conversational notebook

SQL queries are shown in a compact block by default. Users can expand the block to inspect full SQL text.

Expanded SQL block view:

MLJAR Studio can execute SQL blocks, materialize outputs into notebook variables, and continue analysis in Python for visualizations and machine learning workflows.

Troubleshooting

• Connection refused: verify host, port, VPN/network access, and PostgreSQL service status.
• Authentication failed: check username/password and database-level permissions.
• SSL/transport issues: confirm required SSL mode and server certificates.
• Schema/tables not visible: verify schema name, user grants, and table visibility selection in connector save flow.

Related docs

See the Database Connections overview and AI Data Analyst for prompt-driven analysis on top of SQL outputs.