qdrant-edge 0.7.0

A lightweight, in-process vector search engine designed for embedded devices, autonomous systems, and mobile agents.
Documentation 
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use crate::common::types::ScoreType;
use half::f16;
use num_traits::Float;

use crate::segment::data_types::vectors::{DenseVector, VectorElementTypeHalf};
use crate::segment::spaces::metric::Metric;
#[cfg(target_arch = "x86_64")]
use crate::segment::spaces::metric_f16::avx::manhattan::avx_manhattan_similarity_half;
#[cfg(all(target_arch = "aarch64", target_feature = "neon", not(windows)))]
use crate::segment::spaces::metric_f16::neon::manhattan::neon_manhattan_similarity_half;
#[cfg(any(target_arch = "x86", target_arch = "x86_64"))]
use crate::segment::spaces::metric_f16::sse::manhattan::sse_manhattan_similarity_half;
#[cfg(target_arch = "x86_64")]
use crate::segment::spaces::simple::MIN_DIM_SIZE_AVX;
use crate::segment::spaces::simple::{MIN_DIM_SIZE_SIMD, ManhattanMetric};
use crate::segment::types::Distance;

impl Metric<VectorElementTypeHalf> for ManhattanMetric {
    fn distance() -> Distance {
        Distance::Manhattan
    }

    fn similarity(v1: &[VectorElementTypeHalf], v2: &[VectorElementTypeHalf]) -> ScoreType {
        #[cfg(target_arch = "x86_64")]
        {
            if is_x86_feature_detected!("avx")
                && is_x86_feature_detected!("fma")
                && is_x86_feature_detected!("f16c")
                && v1.len() >= MIN_DIM_SIZE_AVX
            {
                return unsafe { avx_manhattan_similarity_half(v1, v2) };
            }
        }

        #[cfg(any(target_arch = "x86", target_arch = "x86_64"))]
        {
            if is_x86_feature_detected!("sse") && v1.len() >= MIN_DIM_SIZE_SIMD {
                return unsafe { sse_manhattan_similarity_half(v1, v2) };
            }
        }

        #[cfg(all(target_arch = "aarch64", target_feature = "neon", not(windows)))]
        {
            if std::arch::is_aarch64_feature_detected!("neon")
                && std::arch::is_aarch64_feature_detected!("fp16")
                && v1.len() >= MIN_DIM_SIZE_SIMD
            {
                return unsafe { neon_manhattan_similarity_half(v1, v2) };
            }
        }

        manhattan_similarity_half(v1, v2)
    }

    fn preprocess(vector: DenseVector) -> DenseVector {
        vector
    }
}

pub fn manhattan_similarity_half(
    v1: &[VectorElementTypeHalf],
    v2: &[VectorElementTypeHalf],
) -> ScoreType {
    -v1.iter()
        .zip(v2)
        .map(|(a, b)| f16::to_f32((a - b).abs()))
        .sum::<f32>()
}