Clusters

Documentation Index

Fetch the complete documentation index at: https://docs.mixpeek.com/docs/llms.txt

Use this file to discover all available pages before exploring further.

Clusters provide warehouse-native grouping, the multimodal equivalent of SQL GROUP BY.

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Create and run clustering jobs

• Create: Click New Cluster, select collections, pick vector or attribute clustering, and configure algorithm params. API: Create Cluster.
• Execute: Run real-time clustering on the Engine or submit as a job for async processing. API: Execute Clustering and Submit Job.
• Inspect: Review centroids, metrics, and members if saved. Download artifacts like parquet paths under Artifacts. API: Get Artifacts.
• List/Get/Delete: Manage clustering configurations and results. API: List, Get, Delete.
• Stream data: Browse cluster centroids and members directly. API: Stream Data.
• Apply enrichment: Attach cluster labels back to a source or target collection at scale. API: Apply Enrichment.

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Visualization

The cluster scatter plot maps reduced coordinates to position and size:

• x, y → point position on the chart
• z (when dimension_reduction.components is 3) → dot size, where larger dots represent higher z-values

This depth-cue approach surfaces the third dimension without requiring a full 3D renderer, making it easy to spot structure that would be lost in a flat 2D projection.

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Tips

• Start with a sample size to validate parameters before full runs.
• Use LLM labeling for human-friendly labels when vectors are dense and unlabeled.
• Set dimension_reduction.components to 3 to see depth-based sizing in the scatter plot.