Refactor README.md to enhance feature descriptions and improve usage instructions for clarity and consistency.

README.md

@@ -16,20 +16,17 @@ models:
 
 This is a browser-based playground for exploring text embeddings, group similarity, and clustering using WebGPU and ONNX models.
 
-## How it works
-- **Input text** in the textarea. Use single newlines (`\n`) to separate lines within a group, and triple newlines (`\n\n\n`) to separate groups.
-- For each line, the following prompt is used:
-  
-  `Instruct: Given a textual input sentence, retrieve relevant categories that best describe it.\nQuery:{your sentence}`
-- All lines in a group are embedded in a single batch call, with pooling set to `mean` and normalization enabled.
-- Each group is averaged to a single embedding.
-- Cosine similarity is calculated between all group embeddings, resulting in a group-by-group similarity matrix.
-- The similarity matrix is visualized as a heatmap using Plotly (color range locked to 0–1).
-
-## K-Means Clustering & UMAP
-- Enter the number of clusters in the **Clusters:** input.
-- Click **Cluster & Show UMAP** to cluster all lines (ignoring groups) using K-Means and visualize the result with UMAP in a scatter plot.
-- After clustering, the textarea is updated to group lines by cluster (triple newlines between clusters), and the heatmap is automatically refreshed to reflect the new groupings.
+## Features
+- **Text search**: Use your browser's search (Ctrl+F) to quickly find and highlight text within the textarea or results.
+- **Text input**: Enter text in the textarea. Use single newlines (`\n`) to separate lines within a group, and triple newlines (`\n\n\n`) to separate groups.
+- **Group similarity heatmap**: Click **Show Similarity Heatmap** to compute and visualize cosine similarity between group embeddings as a heatmap.
+- **Search cluster reordering**: If a group header contains the word `search`, you can control how other groups and lines are ordered relative to the search group using the **Search Cluster Sort Mode** dropdown:
+  - **By Group Similarity**: Orders groups by similarity to the search group, and lines within each group by similarity to the search group embedding.
+  - **By Max Search Line**: Orders lines within each group by their maximum similarity to any line in the search group.
+- **K-Means & Balanced K-Means clustering**: Set the number of clusters and clustering type, then click **Clustering** to group all lines into clusters. The textarea is updated to reflect the new clusters.
+- **UMAP scatter plot**: Click **Cluster Plot** to visualize clusters in 2D using UMAP. Cluster names are shown in the legend.
+- **Cluster naming**: Click **Naming Cluster** to generate descriptive names for each cluster using a text generation model. Names are updated in both the textarea and the scatter plot legend.
+- **Progress bar**: All major actions display a progress bar during processing.
 
 ## Tech stack
 - [@huggingface/transformers](https://www.npmjs.com/package/@huggingface/transformers) (ESM, WebGPU)
@@ -40,7 +37,10 @@ This is a browser-based playground for exploring text embeddings, group similari
 ## Usage
 1. Enter or paste your text in the textarea.
 2. Separate groups with triple newlines if you want to compare group similarity.
-3. Click **Show Similarity Heatmap** to compute and visualize group similarities.
-4. To cluster all lines, set the number of clusters and click **Cluster & Show UMAP**. The textarea and heatmap will update to reflect the new clusters.
+3. (Optional) Use the **Search Cluster Sort Mode** dropdown to control how the search cluster reorders groups/lines.
+4. Click **Show Similarity Heatmap** to compute and visualize group similarities.
+5. To cluster all lines, set the number of clusters and click **Clustering**. The textarea and heatmap will update to reflect the new clusters.
+6. Click **Cluster Plot** to visualize clusters in 2D.
+7. Click **Naming Cluster** to generate descriptive names for each cluster.
 
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