Using Sample Data: The examples on this page use fields from our sample dataset. Download it and create an evaluation with it to follow along with this tutorial. When to Use
Use table widgets to:
- Show detailed breakdowns by multiple dimensions
- Display ranked lists (top/bottom performers)
- Present multi-column statistical summaries
- Create export-friendly data views
- Compare metrics across categories
- Show individual item details
Configuration
Required Fields
Widget display name shown on the dashboard
SeriesQuery returning multiple rows
Query Requirements
Table widgets require a SeriesQuery with:
- At least one item in the
select clause
- Can mix
COLUMN and AGGREGATION expressions
- Supports
filter for row filtering
- Supports
groupBy for dimensional breakdowns
- Supports
limit for top-N results
When using groupBy, all non-aggregated columns in select must appear in the groupBy array.
Creating in the UI
Open Widget Creator
From your dashboard, click “Add Widget” and select “Table”
Turn off Grouping (Optional)
Decide whether to group by columns or not by toggling the “Group By” toggle. If you turn off grouping, you will not be able to aggregate data.When grouping is turned on, we will automatically output the count of the group.
Select Columns
If aggregating, specify which columns to group by, if not grouping select the columns you want to display.
Enter Title
Give your table a descriptive name (e.g., “Average Scores by Agent”)
Add Aggregations (Optional)
Add metrics to display:
- Columns: Select fields from your data
- Aggregations: Add computed metrics (AVG, COUNT, etc.)
Add Filters (Optional)
Add conditions to narrow the data, these conditions will be applied to the data before it is aggregated. Create Widget
Click “Add” to display the table {
"type": "series",
"data": [
{"category": "Quality", "count": 342, "avg_score": 0.89},
{"category": "Accuracy", "count": 298, "avg_score": 0.85},
{"category": "Helpfulness", "count": 411, "avg_score": 0.91}
]
}
data array represents one row, with keys corresponding to column names.
Example Use Cases
Show statistics grouped by agent with multiple metrics.
widget = client.evaluation_dashboards.widgets.create(
dashboard_id=dashboard.id,
title="Performance by Agent",
type="table",
query={
"select": [
{
"expression": {
"type": "COLUMN",
"column": "agent_name",
"source": "data"
}
},
{
"expression": {
"type": "AGGREGATION",
"function": "COUNT",
"column": "*"
}
},
{
"expression": {
"type": "AGGREGATION",
"function": "AVG",
"column": "overall_score",
"source": "data"
}
},
{
"expression": {
"type": "AGGREGATION",
"function": "MIN",
"column": "overall_score",
"source": "data"
}
},
{
"expression": {
"type": "AGGREGATION",
"function": "MAX",
"column": "overall_score",
"source": "data"
}
}
],
"groupBy": ["agent_name"],
}
)
Use Case 2: Top 10 Highest-Scoring Evaluations
Display the best-performing individual evaluations without aggregation.
In the UI sort using the table header, this will not be reflected in the query.
widget = client.evaluation_dashboards.widgets.create(
dashboard_id=dashboard.id,
title="Top 10 Evaluations",
type="table",
query={
"select": [
{
"expression": {
"type": "COLUMN",
"column": "agent_name",
"source": "data"
}
},
{
"expression": {
"type": "COLUMN",
"column": "task_type",
"source": "data"
}
},
{
"expression": {
"type": "COLUMN",
"column": "overall_score",
"source": "data"
}
}
],
"orderBy": [
{
"column": "overall_score",
"direction": "DESC"
}
],
"limit": 10
}
)
Use Case 3: Agent Comparison with Multiple Metrics
Compare agents across several statistical measures including accuracy and relevance.
widget = client.evaluation_dashboards.widgets.create(
dashboard_id=dashboard.id,
title="Agent Performance Comparison",
type="table",
query={
"select": [
{
"expression": {
"type": "COLUMN",
"column": "agent_name",
"source": "data"
}
},
{
"expression": {
"type": "AGGREGATION",
"function": "COUNT",
"column": "*"
}
},
{
"expression": {
"type": "AGGREGATION",
"function": "AVG",
"column": "overall_score",
"source": "data"
}
},
{
"expression": {
"type": "AGGREGATION",
"function": "AVG",
"column": "accuracy_score",
"source": "data"
}
},
{
"expression": {
"type": "AGGREGATION",
"function": "AVG",
"column": "relevance_score",
"source": "data"
}
}
],
"groupBy": ["agent_name"]
}
)
Use Case 4: Low-Scoring Evaluations Review
Show evaluations scoring below 80 that need attention.
widget = client.evaluation_dashboards.widgets.create(
dashboard_id=dashboard.id,
title="Low-Scoring Evaluations",
type="table",
query={
"select": [
{"expression": {"type": "COLUMN", "column": "agent_name", "source": "data"}},
{"expression": {"type": "COLUMN", "column": "task_type", "source": "data"}},
{"expression": {"type": "COLUMN", "column": "overall_score", "source": "data"}},
{"expression": {"type": "COLUMN", "column": "accuracy_score", "source": "data"}}
],
"filter": {
"conditions": [
{
"column": "overall_score",
"source": "data",
"operator": "<",
"value": 80
}
]
}
}
)
