Normalize to speed up distance calculation

[kelindar]

This pull request introduces several optimizations and enhancements to the vector similarity calculations. The most important changes include adding a new dot product function, normalizing vectors before calculations, and updating benchmarks and tests to reflect these changes.

Optimizations and Enhancements:

• internal/cosine/simd/simd.go: Introduced the DotProduct function to calculate the dot product between two vectors, added genericDotProduct for non-hardware implementations, and updated the Cosine function to use genericCosine for non-hardware cases. [1] [2]

• bruteforce.go: Added vector normalization in the Add and Search methods to ensure vectors are unit vectors, allowing the use of dot product for cosine similarity calculations. [1] [2] [3]

Performance Improvements:

• internal/cosine/cosine_apple.c, internal/cosine/cosine_avx.c, internal/cosine/cosine_neon.c: Added the f32_dot_product function to each SIMD implementation to support efficient dot product calculations. [1] [2] [3]

Benchmark and Test Updates:

• internal/cosine/simd/simd_test.go: Updated benchmarks to include dot product performance and added tests for the new DotProduct function. [1] [2]

Miscellaneous:

• bruteforce_test.go: Updated benchmark results to reflect the performance improvements from using dot product and vector normalization.