166c67492a
This commit implements a comprehensive optimization of the Maximal Marginal Relevance (MMR) calculation in the search utilities. The key improvements include: ## Algorithm Improvements - **True MMR Implementation**: Replaced the previous diversity-aware scoring with proper iterative MMR algorithm that greedily selects documents one at a time - **Vectorized Operations**: Leveraged numpy's optimized BLAS operations through matrix multiplication instead of individual dot products - **Adaptive Strategy**: Uses different optimization strategies for small (≤100) and large datasets to balance performance and memory usage ## Performance Optimizations - **Memory Efficiency**: Reduced memory complexity from O(n²) to O(n) for large datasets - **BLAS Optimization**: Proper use of matrix multiplication leverages optimized BLAS libraries - **Batch Normalization**: Added `normalize_embeddings_batch()` for efficient L2 normalization of multiple embeddings at once - **Early Termination**: Stops selection when no candidates meet minimum score threshold ## Key Changes - `maximal_marginal_relevance()`: Complete rewrite with proper iterative MMR algorithm - `normalize_embeddings_batch()`: New function for efficient batch normalization - `_mmr_small_dataset()`: Optimized implementation for small datasets using precomputed similarity matrices - Added comprehensive test suite with 9 test cases covering edge cases, correctness, and performance scenarios ## Benefits - **Correctness**: Now implements true MMR algorithm instead of approximate diversity scoring - **Memory Usage**: O(n) memory complexity vs O(n²) for the original implementation - **Scalability**: Better performance characteristics for large datasets - **Maintainability**: Cleaner, more readable code with comprehensive test coverage 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com> |
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