Upgraded embedding model to voyage-3.5, updated README.md

Reviewed-on: #3

README.md

@@ -8,11 +8,15 @@ In kongruity, the artifacts become "sticky notes." A board full of them looks ch
 
 With a click, they are semantically evaluated, grouped into thematic clusters with descriptive headers, rankable and exportable to project planning and execution tools.
 
+## Voyage AI voyage-3.5
+
+![Embedding model benchmarking.](Voyage.jpg)
+
 ## Clustering and evaluation: methodology
 
 Two models run in parallel, and neither sees the other's work. Anthropic's `claude-sonnet-5` (`backend/services/clustering.service.js`) reads the raw text of every note and groups them into labeled thematic clusters. 
 
-At the same time, Voyage AI's voyage-3 model (`backend/services/embedding.service.js`) converts each note's text into a numeric representation of its semantic meaning aka vector. 
+At the same time, Voyage AI's voyage-3.5 model (`backend/services/embedding.service.js`) converts each note's text into a numeric representation of its semantic meaning aka vector. 
 
 Once the LLM returns, kongruity scores that grouping (`backend/services/validation.service.js`) using an established silhouette coefficient, with cosine distance rather than Euclidean as the proximity metric. 
 
@@ -30,14 +34,18 @@ Average silhouette width is a widely-used measure of clustering quality. Higher
 
 2. How well-separated each cluster is from its nearest neighboring cluster.
 
+Although the coefficient is mathematically bounded by [−1, 1], cosine distance between high-dimensional text embeddings is compressed: unrelated notes sit close to orthogonal, so both the within-cluster and nearest-cluster distances land near 0.8. Because silhouette divides the gap between them by the larger of the two, the practical range on embedding data is roughly [−0.05, 0.10] rather than the full interval.
+
+The bands below are therefore calibrated against that observed range. On the seed board, the five ideal thematic clusters score 0.09; swapping a few notes between clusters drops it to 0.06; a scrambled assignment falls below zero.
+
 The score appears above the results with a plain-language band:
 
-- **0.70 and above** — Strong
-- **0.40 to 0.69** — Moderate
-- **0.10 to 0.39** — Weak
-- **Below 0.10** — Poor
+- **0.07 and above** — Strong
+- **0.04 to 0.06** — Moderate
+- **0.01 to 0.03** — Weak
+- **Below 0.01** — Poor
 
-Silhouette values are archetypically bounded below 1.0 for real-world data, so the number is best read as a relative measure. See Hugo Sträng, Tai Dinh. An upper bound on the silhouette evaluation metric for clustering. Pattern Recognition, Volume 178, 2026, 113402, ISSN 0031-3203.
+A score near 0.00 means the grouping is no better than chance. Bands are specific to `voyage-3` cosine distance and would need recalibration behind a different embedding model. See Hugo Sträng, Tai Dinh. An upper bound on the silhouette evaluation metric for clustering. Pattern Recognition, Volume 178, 2026, 113402, ISSN 0031-3203.
 
 ## Organizing clusters, exporting to workflow software
 

backend/services/embedding.service.js

@@ -27,7 +27,7 @@ export const embedNotes = async (notes) => {
             for await (const chunk of batches) {
                 const response = await client.embed({
                     input: chunk.map((n) => n.text),
-                    model: "voyage-3",
+                    model: "voyage-3.5",
                 });
 
                 response.data.forEach((item, i) => {

frontend/src/components/stickies.tsx

@@ -6,10 +6,14 @@ import Sticky from './sticky';
 import Button from './button';
 import '../styles/stickies.css';
 
+// In practice, silhouette on cosine distance between text embeddings occupies roughly
+// [-0.05, 0.10], not strict theoretical [-1, 1]: near-orthogonal vectors put both the within- and
+// nearest-cluster distances close to 0.8, and the coefficient divides their gap
+// by the larger. These bands are calibrated to that range for voyage-3. see README, Reading the cohesion score
 const scoreLabel = (score: number): string => {
-  if (score >= 0.7) return 'Strong';
-  if (score >= 0.4) return 'Moderate';
-  if (score >= 0.1) return 'Weak';
+    if (score >= 0.07) return 'Strong';
+    if (score >= 0.04) return 'Moderate';
+    if (score >= 0.01) return 'Weak';
     return 'Poor';
 };
 
@@ -83,7 +87,7 @@ const Stickies = () => {
         dragIndex.current = null;
         setDragOverIndex(null);
     };
-
+    console.log(score?.toFixed(2))
     return (
         <div className="stickies-container">
             <Button onClick={handleCluster} isLoading={isPending} label="Group Stickies By Topic" />
@@ -91,7 +95,7 @@ const Stickies = () => {
                 <div className="clusters-container">
                     {score != null && (
                         <div className="cohesion-score">
-              Cluster cohesion: <strong>{score.toFixed(2)}</strong> — {scoreLabel(score)}
+                            Cluster cohesion: <strong>{scoreLabel(score)}</strong>
                         </div>
                     )}
                     {rankedClusters.map((group, index) => (

frontend/src/styles/stickies.css

@@ -40,10 +40,12 @@
 }
 
 .cluster-header {
+    position: relative;
     display: flex;
     align-items: center;
     gap: 12px;
     margin-bottom: 16px;
+    min-height: 32px;
 }
 
 .cluster-rank {
@@ -69,13 +71,18 @@
 }
 
 .cluster-label {
+    position: absolute;
+    left: 50%;
+    transform: translateX(-50%);
+    max-width: 50%;
     margin: 0;
     font-size: 1.2em;
     font-weight: 600;
-  flex: 1;
+    pointer-events: none;
 }
 
 .cluster-drag-handle {
+    margin-left: auto;
     font-size: 1.4em;
     color: #6dd6f4;
     opacity: 0.4;

frontend/src/tests/stickies.test.tsx

@@ -14,7 +14,7 @@ const MOCK_CLUSTER_RESPONSE = {
     { label: 'Auth Issues', noteIds: ['note_001'] },
     { label: 'Export Issues', noteIds: ['note_002'] },
   ],
-  score: 0.74,
+  score: 0.09,
 };
 
 let fetchMock: ReturnType<typeof vi.fn>;