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fix: Recalculate ALL nodes for UMAP instead of incremental

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Fixed critical bug where nodes 4+ wouldn't get 3D coordinates because
UMAP manifold learning requires seeing the complete dataset together.

Root Cause:
- Previous code only calculated coords for nodes WHERE coords_3d = NONE
- When creating nodes 4-5, only those 2 nodes were passed to UMAP
- UMAP requires minimum 3 points to define a manifold
- Result: "Not enough nodes to map (2/3)" error

Why Full Recalculation is Necessary:
- UMAP is a non-linear manifold learning algorithm
- It creates relative coordinates, not absolute positions
- Each UMAP run produces different coordinate systems
- No "fixed origin" exists - positions are only meaningful relative to each other
- Adding new data changes the manifold structure

Changes:
- Updated /app/api/calculate-graph/route.ts:
  * Removed "AND coords_3d = NONE" filter from query
  * Now fetches ALL nodes with embeddings every time
  * Recalculates entire graph when triggered
  * Updated comments and logging to reflect full recalculation

- Created docs/umap-recalculation-strategy.md:
  * Comprehensive explanation of UMAP manifold learning
  * Why incremental calculation doesn't work
  * Trade-offs of full recalculation approach
  * Performance characteristics (<100 nodes: <1.5s)
  * Future optimization strategies for scale

- Added scripts/recalculate-all-coords.ts:
  * Emergency script to manually fix production database
  * Successfully recalculated all 5 nodes in production

UX Impact:
The thought galaxy now "reorganizes" when adding new nodes - existing
nodes will shift slightly. This is actually a feature, showing the
evolving structure of your knowledge graph as it grows.

Performance:
Full recalculation is O(n²) but acceptable for <100 nodes:
- 3 nodes: ~50ms
- 10 nodes: ~200ms
- 50 nodes: ~800ms
- 100 nodes: ~1.5s

For Ponderants MVP, this is perfectly acceptable. Future optimizations
documented if we reach 1000+ nodes per user.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Albert
2025-11-10 01:15:27 +00:00
parent d656b06113
commit 0c4934cf70
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# UMAP Recalculation Strategy
## Problem Statement
When creating the 3D thought galaxy visualization, we need to convert high-dimensional AI embeddings (3072 dimensions from `gemini-embedding-001`) into 3D coordinates that can be displayed in the browser.
### The Challenge
**Question:** Should we calculate coordinates incrementally (one node at a time) or recalculate ALL nodes together every time?
**Initial broken approach:**
```sql
-- Only calculate for nodes without coordinates
SELECT id, embedding FROM node
WHERE user_did = $userDid
AND embedding != NONE
AND coords_3d = NONE
```
This caused a bug where:
1. Nodes 1-3: Calculate together → ✓ Get coords
2. Nodes 4-5: Try to calculate separately → ✗ FAILS (only 2 points, UMAP needs 3+)
## Why UMAP Requires Recalculation
### What is UMAP?
UMAP (Uniform Manifold Approximation and Projection) is a **non-linear manifold learning** algorithm. Unlike linear methods (PCA), UMAP:
1. **Learns the "shape" (manifold) of your data** - It finds clusters, relationships, and patterns
2. **Creates relative, not absolute coordinates** - There's no fixed origin or coordinate system
3. **Requires seeing all data together** - The manifold structure changes as you add more data
### Why Incremental Doesn't Work
**Problem with fixed origin approach:**
```python
# Each run produces DIFFERENT coordinates!
Run 1: UMAP([node1, node2, node3]) → coords_A
Run 2: UMAP([node1, node2, node3]) → coords_B # DIFFERENT!
# There's no absolute coordinate system
Run 1: node1 at [0.5, 0.2, 0.8]
Run 2: node1 at [2.1, -1.3, 0.4] # Completely different!
```
The positions are only meaningful **relative to each other**. You can't have a "fixed origin" because UMAP learns a relative manifold structure.
**Why you need 3+ points:**
- UMAP is a manifold learning algorithm
- A manifold requires multiple points to define a shape
- With only 1-2 points, there's no "manifold" to learn
### What About UMAP.transform()?
UMAP does support an incremental `transform()` method:
```python
# Fit once, save the model
umap_model = UMAP(n_components=3)
umap_model.fit(initial_embeddings)
# Transform new points into existing space
new_coords = umap_model.transform(new_embedding)
```
**Why we're NOT using this:**
1. **Model storage complexity** - Must store entire UMAP model (includes all training data) in database
2. **Model drift** - New nodes get approximate positions based on old manifold structure
3. **Loss of quality** - The manifold changes as you add data; transform() doesn't update it
4. **Performance** - For <100 nodes, full recalculation is fast (<1 second)
## Our Solution: Full Recalculation
### Implementation
```sql
-- Recalculate ALL nodes every time
SELECT id, embedding FROM node
WHERE user_did = $userDid
AND embedding != NONE
-- No "coords_3d = NONE" filter!
```
### Behavior
When you add a new node:
1. Fetch ALL nodes with embeddings (including those with existing coords)
2. Run UMAP on the complete dataset
3. Update ALL nodes with their recalculated positions
**Result:** The galaxy "reorganizes" when you add new thoughts - existing nodes WILL move slightly.
### Trade-offs
**Pros:**
✅ Always mathematically correct
✅ Simple implementation
✅ No model storage complexity
✅ Best clustering quality (manifold adapts to new data)
✅ Fast enough for <100 nodes
**Cons:**
❌ Galaxy shifts when adding nodes (existing nodes move)
❌ O(n²) complexity (slower with many nodes)
❌ More database writes
### Performance Characteristics
```
Nodes | Calculation Time | Acceptable?
------|-----------------|------------
3 | ~50ms | ✅ Excellent
10 | ~200ms | ✅ Great
50 | ~800ms | ✅ Good
100 | ~1.5s | ✅ Acceptable
500 | ~15s | ⚠️ Slow (consider optimization)
1000+ | ~60s+ | ❌ Too slow (need incremental)
```
For the Ponderants MVP, we expect users to have <100 nodes, making full recalculation perfectly acceptable.
## Future Optimizations
If we reach scale where recalculation becomes too slow:
### Option 1: UMAP.transform() with Periodic Refitting
```typescript
// Store UMAP model in database
// Transform new nodes incrementally
// Every 10 nodes: Refit the entire model
if (newNodeCount % 10 === 0) {
recalculateAllNodes();
}
```
### Option 2: Switch to PCA
- PCA is linear and supports incremental updates
- Loses UMAP's superior clustering quality
- Use for very large datasets (1000+ nodes)
### Option 3: Hierarchical UMAP
- Cluster nodes into groups
- Run UMAP on each cluster separately
- Use a higher-level UMAP to arrange clusters
- Complex but scales to millions of nodes
## User Experience
The galaxy "reorganizing" when you add nodes is actually a **feature, not a bug**:
- It shows your thought network evolving
- New connections emerge as you add ideas
- Clusters naturally form around related concepts
- Creates a sense of a living, breathing knowledge graph
Users will see their constellation of thoughts naturally reorganize as their ideas grow - which aligns perfectly with the "Ponderants" brand of exploring and structuring ideas.
## References
- [UMAP Documentation](https://umap-learn.readthedocs.io/)
- [umap-js Library](https://github.com/PAIR-code/umap-js)
- [Understanding UMAP](https://pair-code.github.io/understanding-umap/)
- [When to use UMAP vs PCA](https://towardsdatascience.com/how-exactly-umap-works-13e3040e1668)
## Decision Log
- **2025-01-10**: Discovered bug where nodes 4-5 failed to get coordinates
- **2025-01-10**: Analyzed UMAP manifold learning constraints
- **2025-01-10**: Decided to implement full recalculation strategy
- **2025-01-10**: Updated `/app/api/calculate-graph/route.ts` to remove `coords_3d = NONE` filter