Embedding Dimension Reduction Analysis

Analyze the trade-offs of embedding dimension reduction for our system.

## Current Setup
- Model: {{embedding_model}}
- Dimensions: {{current_dimensions}}
- Vector count: {{vector_count}}

## Constraints
- Storage budget: {{storage_budget}}
- Latency target: {{latency_target}}

Evaluate dimension reduction techniques:

**PCA Analysis**
- Variance retention at different dimensions
- Quality impact on retrieval
- Computational overhead

**Matryoshka Embeddings**
- Native dimension flexibility
- Quality at reduced dimensions
- Implementation considerations

**Quantization Alternatives**
- Binary quantization
- Product quantization
- Scalar quantization

Provide:
- Recommended approach with justification
- Expected storage savings
- Quality impact quantification
- Implementation plan