Title: perf: Accelerate vector similarity calculations using Rust/SIMD
Body:
Problem
Vector operations such as cosine similarity and Euclidean distance calculation in src/vectorwave/database/db_search.py (check_semantic_drift) and src/vectorwave/utils/replayer_semantic.py are currently implemented in pure Python. These operations become a bottleneck when processing high-dimensional vectors (e.g., 1536 dims) or large batches of logs.
Proposed Solution
Offload heavy mathematical computations to Rust.
Implement vector distance functions using Rust libraries like ndarray or utilize SIMD instructions for maximum performance.
Expose these functions to Python via PyO3.
Expected Outcome
Faster execution of Semantic Drift detection and Semantic Replay tests.
Reduced CPU usage during vector comparisons.
Title: perf: Accelerate vector similarity calculations using Rust/SIMD
Body:
Problem
Vector operations such as cosine similarity and Euclidean distance calculation in src/vectorwave/database/db_search.py (check_semantic_drift) and src/vectorwave/utils/replayer_semantic.py are currently implemented in pure Python. These operations become a bottleneck when processing high-dimensional vectors (e.g., 1536 dims) or large batches of logs.
Proposed Solution
Offload heavy mathematical computations to Rust.
Implement vector distance functions using Rust libraries like ndarray or utilize SIMD instructions for maximum performance.
Expose these functions to Python via PyO3.
Expected Outcome
Faster execution of Semantic Drift detection and Semantic Replay tests.
Reduced CPU usage during vector comparisons.