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.