velesdb-core 1.7.2

High-performance vector database engine written in Rust
Documentation 
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//! Tests for batch and multi-query search methods.

#![cfg(all(test, feature = "persistence"))]

use crate::collection::types::Collection;
use crate::distance::DistanceMetric;
use crate::filter::{Condition, Filter};
use crate::point::Point;
use std::path::PathBuf;

/// Helper: create a collection with 6 test points spanning the unit hypercube.
fn setup_batch_collection() -> (tempfile::TempDir, Collection) {
    let dir = tempfile::tempdir().expect("temp dir");
    let col = Collection::create(PathBuf::from(dir.path()), 4, DistanceMetric::Cosine)
        .expect("create collection");

    let points = vec![
        make_point(1, vec![1.0, 0.0, 0.0, 0.0], "tech"),
        make_point(2, vec![0.0, 1.0, 0.0, 0.0], "sports"),
        make_point(3, vec![0.7, 0.7, 0.0, 0.0], "tech"),
        make_point(4, vec![0.0, 0.0, 1.0, 0.0], "music"),
        make_point(5, vec![0.5, 0.5, 0.5, 0.5], "tech"),
        make_point(6, vec![0.9, 0.1, 0.0, 0.0], "sports"),
    ];
    col.upsert(points).expect("upsert");
    (dir, col)
}

fn make_point(id: u64, vector: Vec<f32>, category: &str) -> Point {
    Point {
        id,
        vector,
        payload: Some(serde_json::json!({ "category": category })),
        sparse_vectors: None,
    }
}

// -----------------------------------------------------------------------
// search_batch_parallel
// -----------------------------------------------------------------------

#[test]
fn test_batch_parallel_two_queries_returns_per_query_results() {
    let (_dir, col) = setup_batch_collection();

    let q1: Vec<f32> = vec![1.0, 0.0, 0.0, 0.0];
    let q2: Vec<f32> = vec![0.0, 1.0, 0.0, 0.0];
    let queries: Vec<&[f32]> = vec![&q1, &q2];

    let results = col
        .search_batch_parallel(&queries, 3)
        .expect("batch search");

    assert_eq!(results.len(), 2, "one result set per query");
    assert!(!results[0].is_empty(), "first query should have results");
    assert!(!results[1].is_empty(), "second query should have results");

    // First query is close to point 1 ([1,0,0,0]).
    assert_eq!(results[0][0].point.id, 1, "q1 best match should be id=1");
    // Second query is close to point 2 ([0,1,0,0]).
    assert_eq!(results[1][0].point.id, 2, "q2 best match should be id=2");
}

// -----------------------------------------------------------------------
// search_batch_with_filters — per-query filters
// -----------------------------------------------------------------------

#[test]
fn test_batch_with_filters_applies_distinct_filter_per_query() {
    let (_dir, col) = setup_batch_collection();

    let q1: Vec<f32> = vec![1.0, 0.0, 0.0, 0.0];
    let q2: Vec<f32> = vec![0.0, 1.0, 0.0, 0.0];
    let queries: Vec<&[f32]> = vec![&q1, &q2];

    let filter_tech = Some(Filter::new(Condition::eq("category", "tech")));
    let filter_sports = Some(Filter::new(Condition::eq("category", "sports")));
    let filters = vec![filter_tech, filter_sports];

    let results = col
        .search_batch_with_filters(&queries, 5, &filters)
        .expect("batch with filters");

    assert_eq!(results.len(), 2);

    // All q1 results should be category=tech.
    for r in &results[0] {
        let cat = r.point.payload.as_ref().and_then(|p| p.get("category"));
        assert_eq!(cat.and_then(|v| v.as_str()), Some("tech"));
    }
    // All q2 results should be category=sports.
    for r in &results[1] {
        let cat = r.point.payload.as_ref().and_then(|p| p.get("category"));
        assert_eq!(cat.and_then(|v| v.as_str()), Some("sports"));
    }
}

// -----------------------------------------------------------------------
// search_batch_with_filters — mismatched lengths
// -----------------------------------------------------------------------

#[test]
fn test_batch_with_filters_rejects_mismatched_lengths() {
    let (_dir, col) = setup_batch_collection();

    let q1: Vec<f32> = vec![1.0, 0.0, 0.0, 0.0];
    let queries: Vec<&[f32]> = vec![&q1];
    let filters = vec![None, None]; // 2 filters for 1 query

    let result = col.search_batch_with_filters(&queries, 5, &filters);
    assert!(result.is_err(), "should reject mismatched lengths");
}

// -----------------------------------------------------------------------
// Batch search on empty collection
// -----------------------------------------------------------------------

#[test]
fn test_batch_parallel_empty_collection_returns_empty_vecs() {
    let dir = tempfile::tempdir().expect("temp dir");
    let col = Collection::create(PathBuf::from(dir.path()), 4, DistanceMetric::Cosine)
        .expect("create collection");

    let q: Vec<f32> = vec![1.0, 0.0, 0.0, 0.0];
    let queries: Vec<&[f32]> = vec![&q];

    let results = col
        .search_batch_parallel(&queries, 5)
        .expect("batch search on empty");

    assert_eq!(results.len(), 1);
    assert!(
        results[0].is_empty(),
        "empty collection should return no hits"
    );
}

// -----------------------------------------------------------------------
// Dimension mismatch
// -----------------------------------------------------------------------

#[test]
fn test_batch_parallel_rejects_wrong_dimension() {
    let (_dir, col) = setup_batch_collection();

    let bad_q: Vec<f32> = vec![1.0, 0.0]; // dim=2, expected=4
    let queries: Vec<&[f32]> = vec![&bad_q];

    let result = col.search_batch_parallel(&queries, 3);
    assert!(result.is_err(), "should reject wrong dimension");
}