Embedding Similarity Debugger

Debug embedding similarity issues in the retrieval system.

## Problematic Query-Document Pairs
{{problematic_pairs}}

## Expected vs Actual Similarity
{{similarity_discrepancies}}

## Embedding Configuration
{{embedding_config}}

Build a debugging analysis:

```python
class EmbeddingSimilarityDebugger:
    def analyze_pair(self, query: str, document: str) -> DebugReport:
        """
        Analyze:
        - Token overlap
        - Semantic relationship
        - Embedding space positioning
        """
        pass
    
    def compare_with_neighbors(self, embedding: np.ndarray, k: int) -> List[Neighbor]:
        """Find nearest neighbors for context"""
        pass
    
    def identify_failure_pattern(self, pairs: List[ProblemPair]) -> FailurePattern:
        """
        Patterns:
        - Vocabulary mismatch
        - Semantic gap
        - Length disparity
        - Domain shift
        """
        pass
    
    def suggest_improvements(self, pattern: FailurePattern) -> List[Improvement]:
        """Recommend fixes"""
        pass
```

Include:
- Visualization tools
- Root cause categorization
- Actionable recommendations