qdrant-edge 0.7.2

A lightweight, in-process vector search engine designed for embedded devices, autonomous systems, and mobile agents.
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82

use std::sync::atomic::AtomicBool;

use crate::common::counter::hardware_accumulator::HwMeasurementAcc;

use crate::sparse::SearchScratch;
use crate::sparse::common::sparse_vector::RemappedSparseVector;
use crate::sparse::index::inverted_index::InvertedIndex;
use crate::sparse::index::search_context::SearchContext;
use crate::sparse::index::tests::common::{build_index, match_all};

fn query<I: InvertedIndex>(index: &I, query: RemappedSparseVector) {
    let is_stopped = AtomicBool::new(false);
    let accumulator = HwMeasurementAcc::new();
    let hardware_counter = accumulator.get_counter_cell();
    let top = 10;
    let mut scratch = SearchScratch::new_for_test();
    let mut search_context = SearchContext::new(
        query.clone(),
        top,
        index,
        &mut scratch,
        &is_stopped,
        &hardware_counter,
    )
    .unwrap();

    let result = search_context.search(&match_all);
    let docs: Vec<_> = result.iter().map(|x| x.idx).collect();
    drop(search_context);

    let mut search_context = SearchContext::new(
        query,
        top,
        index,
        &mut scratch,
        &is_stopped,
        &hardware_counter,
    )
    .unwrap();
    let plain_result = search_context.plain_search(&docs);

    assert_eq!(result, plain_result);
}

/// Run regular search and then palin search and compare the results
#[test]
fn test_search_vs_plain() {
    let count = 1000;
    let density = 32;
    let vocab1 = 32;
    let vocab2 = 512;

    // Expected posting length = count * density / vocab1 / 2 = 1000 * 32 / 32 / 2 = 500
    // Expected posting length = count * density / vocab2 / 2 = 1000 * 32 / 512 / 2 = 62.5

    let index = build_index::<f32>(count, density, vocab1, vocab2);

    let freq_query = RemappedSparseVector {
        indices: vec![1],
        values: vec![1.0],
    };

    let freq_query2 = RemappedSparseVector {
        indices: vec![0, 1, 2],
        values: vec![1.0, 1.0, 1.0],
    };

    let infreq_query = RemappedSparseVector {
        indices: vec![100],
        values: vec![1.0],
    };

    let infreq_query2 = RemappedSparseVector {
        indices: vec![101, 102, 103],
        values: vec![1.0, 1.0, 1.0],
    };

    query(&index.index, freq_query);
    query(&index.index, freq_query2);
    query(&index.index, infreq_query);
    query(&index.index, infreq_query2);
}