Embedding Update Pipeline

Design an embedding update pipeline for refreshing stale embeddings.

## Update Triggers
{{update_triggers}}

## Corpus Size
{{corpus_size}}

## Update Constraints
{{update_constraints}}

Implement the pipeline:

```python
class EmbeddingUpdatePipeline:
    def detect_stale_documents(self, last_update: datetime) -> List[str]:
        """Find documents needing re-embedding"""
        pass
    
    def prioritize_updates(self, doc_ids: List[str]) -> List[str]:
        """
        Priority factors:
        - Access frequency
        - Content change magnitude
        - Query relevance
        """
        pass
    
    def batch_update(self, doc_ids: List[str], batch_size: int) -> UpdateResult:
        """Process updates in batches"""
        pass
    
    def validate_update(self, doc_id: str, old_embedding: np.ndarray, new_embedding: np.ndarray) -> bool:
        """Validate embedding quality"""
        pass
```

Include:
- Incremental update support
- Rollback capability
- Progress tracking
- Quality gates