Embedding Visualization and Analysis

Create an embedding analysis and visualization system.

Embedding details:
- Embedding type: [WORD/SENTENCE/IMAGE/USER]
- Dimensions: [EMBEDDING SIZE]
- Number of points: [DATASET SIZE]
- Analysis goals: [CLUSTERING/SIMILARITY/QUALITY]

Visualization requirements:
1. Dimensionality reduction:
   - t-SNE configuration
   - UMAP optimization
   - PCA baselines
2. Visualization:
   - 2D/3D scatter plots
   - Interactive exploration
   - Color by metadata
3. Clustering analysis:
   - K-means, DBSCAN
   - Cluster quality metrics
   - Optimal cluster number
4. Similarity analysis:
   - Nearest neighbors
   - Similarity distributions
   - Analogy testing
5. Quality metrics:
   - Intrinsic evaluation
   - Downstream task proxy
   - Alignment scores
6. Comparison:
   - Multiple embedding comparison
   - Alignment visualization
   - Difference analysis
7. Export:
   - Interactive dashboards
   - Report generation
   - Embedding browser

Handle large-scale embeddings efficiently.