vicinity 0.8.1

//! OPT-SNG search algorithm.

use crate::simd;
use crate::sng::graph::SNGIndex;
use crate::RetrieveError;
use std::collections::BinaryHeap;

/// Candidate during search.
#[derive(Clone, PartialEq)]
struct SearchCandidate {
    id: u32,
    distance: f32,
}

impl Eq for SearchCandidate {}

impl PartialOrd for SearchCandidate {
    fn partial_cmp(&self, other: &Self) -> Option<std::cmp::Ordering> {
        Some(self.cmp(other))
    }
}

impl Ord for SearchCandidate {
    fn cmp(&self, other: &Self) -> std::cmp::Ordering {
        // Use total_cmp for IEEE 754 total ordering (NaN-safe)
        self.distance.total_cmp(&other.distance).reverse()
    }
}

/// Search OPT-SNG graph for k nearest neighbors.
///
/// Uses greedy search with early termination, leveraging the theoretical
/// guarantee of O(log n) search path length.
pub fn search_sng(
    index: &SNGIndex,
    query: &[f32],
    k: usize,
) -> Result<Vec<(u32, f32)>, RetrieveError> {
    if index.num_vectors == 0 {
        return Ok(Vec::new());
    }

    // Start from random entry point (or first vector)
    let mut current = 0u32;
    let mut current_dist = f32::INFINITY;

    // Find good starting point
    for i in 0..index.num_vectors.min(100) {
        let vec = index.get_vector(i);
        let dist = 1.0 - simd::dot(query, vec);
        if dist < current_dist {
            current_dist = dist;
            current = i as u32;
        }
    }

    // Dense generation-counter visited set (O(1) insert/lookup, O(1) clear).
    thread_local! {
        static VISITED: std::cell::RefCell<(Vec<u8>, u8)> =
            const { std::cell::RefCell::new((Vec::new(), 1)) };
    }

    VISITED.with(|cell| {
        let (marks, gen) = &mut *cell.borrow_mut();
        let num_vectors = index.num_vectors;
        if marks.len() < num_vectors {
            marks.resize(num_vectors, 0);
        }
        if let Some(next) = gen.checked_add(1) {
            *gen = next;
        } else {
            marks.fill(0);
            *gen = 1;
        }
        let generation = *gen;

        let mut visited_insert = |id: u32| -> bool {
            let idx = id as usize;
            if idx < marks.len() && marks[idx] != generation {
                marks[idx] = generation;
                true
            } else { idx >= marks.len() }
        };

        // Greedy search with early termination
        let mut candidates = BinaryHeap::new();
        let mut results = Vec::new();

        candidates.push(SearchCandidate {
            id: current,
            distance: current_dist,
        });
        visited_insert(current);

        // Search with O(log n) guarantee
        let max_iterations = (index.num_vectors as f32).ln().ceil() as usize * 10;
        let mut iterations = 0;

        while let Some(candidate) = candidates.pop() {
            // All candidates in the heap were already marked visited when pushed.
            results.push((candidate.id, candidate.distance));

            if results.len() >= k {
                break;
            }

            if iterations >= max_iterations {
                break;
            }
            iterations += 1;

            // Explore neighbors
            if let Some(neighbors) = index.neighbors.get(candidate.id as usize) {
                for (i, &neighbor_id) in neighbors.iter().enumerate() {
                    // Prefetch next neighbor's vector
                    if i + 1 < neighbors.len() {
                        let next_id = neighbors[i + 1] as usize;
                        let ptr = index.vectors.as_ptr().wrapping_add(next_id * index.dimension);
                        #[cfg(target_arch = "aarch64")]
                        unsafe {
                            std::arch::asm!("prfm pldl1keep, [{ptr}]", ptr = in(reg) ptr, options(nostack, preserves_flags));
                        }
                        #[cfg(target_arch = "x86_64")]
                        unsafe {
                            std::arch::x86_64::_mm_prefetch(ptr as *const i8, std::arch::x86_64::_MM_HINT_T0);
                        }
                    }

                    if !visited_insert(neighbor_id) {
                        continue;
                    }

                    let neighbor_vec = index.get_vector(neighbor_id as usize);
                    let dist = 1.0 - simd::dot(query, neighbor_vec);
                    candidates.push(SearchCandidate {
                        id: neighbor_id,
                        distance: dist,
                    });
                }
            }
        }

        // Sort by distance and return top k
        results.sort_unstable_by(|a, b| a.1.total_cmp(&b.1));
        Ok(results.into_iter().take(k).collect())
    })
}