v5
latestOpenAPI 3.1.02026-08-025631,1012.8 MBGet Latest Cluster Execution
Get the most recent execution results for a cluster.
Returns execution metadata including:
- Execution status (pending, processing, completed, failed)
- Clustering metrics (silhouette score, Davies-Bouldin index, etc.)
- Number of clusters found and documents processed
- Centroid information with labels and summaries
- Execution timestamps
Useful for:
- Displaying cluster statistics in dashboards
- Showing cluster quality metrics to users
- Rendering cluster labels and summaries in the UI
- Tracking execution status and errors
Path parameters
Cluster ID
Cluster ID
Query parameters
Include each centroid's embedding vector (centroids[].centroid_vector). Opt-in: vectors are large (~120KB for 15x1024-dim centroids), so they are omitted from the response entirely unless this is true. Lets clients run vector-input searches with a centroid as the query even when the centroid document is no longer in the vector store.
Include each centroid's embedding vector (centroids[].centroid_vector). Opt-in: vectors are large (~120KB for 15x1024-dim centroids), so they are omitted from the response entirely unless this is true. Lets clients run vector-input searches with a centroid as the query even when the centroid document is no longer in the vector store.
Response
Successful Response
Example response
{
"centroids": [
{
"cluster_id": "cl_0",
"keywords": [
"product",
"review",
"quality"
],
"label": "Product Reviews",
"num_members": 45,
"summary": "Customer feedback about products"
},
{
"cluster_id": "cl_1",
"keywords": [
"help",
"issue",
"support"
],
"label": "Support Tickets",
"num_members": 35,
"summary": "Technical support requests"
},
{
"cluster_id": "cl_2",
"keywords": [
"feature",
"request",
"suggestion"
],
"label": "Feature Requests",
"num_members": 20,
"summary": "User feature suggestions"
}
],
"cluster_id": "clust_ae3e28a429",
"completed_at": "2025-11-13T13:25:40.122000Z",
"created_at": "2025-11-13T13:20:40.122000Z",
"description": "Completed execution with excellent metrics",
"metrics": {
"calinski_harabasz_score": 1234.56,
"davies_bouldin_index": 0.42,
"silhouette_score": 0.85
},
"num_clusters": 3,
"num_points": 100,
"run_id": "run_a8e270953254754b",
"status": "completed"
}