Embedding Maps Workflows#

Use embedding-map workflows when embedding structure, trajectories, or scalar value overlays must be inspected, compared, or visualized.

Install design-research-analysis[maps,embeddings] when records begin as text and the package should create sentence embeddings. Install [maps] alone when you already have numeric vectors or use the CLI’s --feature-columns path. PCA projection and plotting with a custom embedder are available from the base install; maps supplies the additional manifold and clustering backends.

Typical Questions#

  • Do records cluster by condition, role, or phase?

  • Are semantic spaces separable across treatments?

  • Which map best preserves interpretable structure and trajectory legibility?

  • How do traces move through the same map as value signals change?

Key API Entry Points#

Map-Space Diagnostics#

Coverage and trajectory metrics can be computed on raw embeddings or on a lower-dimensional embedding map. Prefer map-space metrics when you want summaries that line up directly with plotted coordinates and CLI exports. Prefer raw embedding-space metrics when preserving the full geometry matters more than human-readable visuals.

CLI Path#

design-research-analysis run-embedding-maps \
  --input data/events.csv \
  --summary-json artifacts/embedding_maps.json \
  --map-csv artifacts/embedding_maps.csv \
  --method pca \
  --method umap \
  --trace-column session_id \
  --order-column timestamp \
  --comparison-png artifacts/embedding_maps.png

The embedding-maps summary JSON includes per-method clustering, coverage, and trajectory diagnostics. When trace and order columns are supplied, trajectories follow those fields; otherwise the CLI falls back to session_id and timestamp when present.

Because the command embeds the text column when --feature-columns is omitted, the command shown above requires both maps and embeddings.