In the Admin Dashboard you can find the Jobs module that lets you track the progress and history of all background tasks that build, refresh, and maintain your Ozgar AI knowledge base. Use it to ensure data ingestion, indexing, and documentation generation workflows are running smoothly and to diagnose any failures.

 

1) Page Layout

  • Tabs

    • Data Refresh (default): Live view of all in-flight jobs preparing your knowledge base.

    • History: Archive of completed and failed jobs for auditing and troubleshooting.

  • Knowledge Base Status
    In the upper-right corner, a status indicator shows the current health of the knowledge base (e.g. “Refreshing,” “Ready,” or “Error”).

 

 

2) Data Refresh Tab

Summary Panel

At the top, a progress bar and breakdown list visualize the current refresh run:

  • Overall Progress (e.g. “Loading And Preparing Data… ”)

  • Stages

    • Source Code Loading • Files loaded

    • Database Loading • Database objects loaded

    • Fact Generation • Facts generated

    • Knowledge Summarization • Summaries generated

    • Embedding & Indexing • Documents indexed

    • Knowledge Graph Preparation • Nodes & edges added

 

Each line shows a spinner or checkmark and live counts, plus an estimated time remaining.

 

 

3) Job List

Below the summary, a filterable table displays individual jobs contributing to the current refresh:

Column

Description

Run ID

Unique identifier; click to drill into job details/logs.

Name

Descriptive name (e.g. “AS/400 sources,” “Page generator”).

Type

Job category (Connector, Page Generator, Indexer, etc.).

Started

Timestamp when the job began.

Elapsed

Duration since start.

Status

In Progress • Succeeded • Failed (with error details if applicable).
Use the Search, Job Type, and Status filters to narrow down running tasks.

 

 

4) History Tab

The History tab shows a similar table of all past jobs:

  • Completed Runs – review success/failure rates and durations.

  • Error Details – click any failed job’s Run ID to inspect stack traces and retries.

  • Time Filters – quickly jump to jobs from “Last 24 Hours,” “Last Week,” or a custom date range.

 

 

5) Best Practices

  • Monitor Regularly: Keep an eye on the Data Refresh progress after major code or schema changes.

  • Inspect Logs: Drill into individual job details to identify root causes — missing connectors, malformed code files, or load-timeouts.

  • Schedule Off-Peak Runs: If ingesting large volumes, schedule refresh jobs during low-traffic windows to minimize impact on system performance.

 

By leveraging the Jobs module, you ensure your Ozgar AI knowledge base remains current, reliable, and transparent—empowering your team with confidence that every code change, database update, and documentation refresh completes successfully.