//! Greedy search algorithm for DiskANN.
//!
//! **Search Strategy:** Beam search with best-first expansion.
//!
//! # Algorithm Overview
//!
//! ```text
//! GreedySearch(query, k, L, medoid):
//!   1. visited = {}
//!   2. candidates = PriorityQueue(max_size=L)  // Min-heap by distance
//!   3. candidates.push(medoid)
//!   4. best = PriorityQueue(max_size=k)        // Top-k results
//!
//!   5. While candidates is not empty:
//!        current = candidates.pop_closest()
//!        visited.add(current)
//!        best.push(current)
//!
//!        For each neighbor of current:
//!          if neighbor not in visited:
//!            dist = distance(query, neighbor)
//!            candidates.push(neighbor, dist)
//!            // Keep only L closest candidates (beam width)
//!            if |candidates| > L:
//!              candidates.remove_farthest()
//!
//!   6. Return best.top_k()
//! ```
//!
//! # Performance Characteristics
//!
//! - **Time complexity**: O(L * avg_degree * dim)
//! - **Space complexity**: O(L + k)
//! - **Independence**: Search time does NOT depend on dataset size (graph property!)
//!
//! # Tuning Search List Size (L)
//!
//! | L | Recall@10 | Search Time | Use Case |
//! |---|-----------|-------------|----------|
//! | 50 | 85% | 1ms | Real-time apps (latency-critical) |
//! | 75 | 92% | 2ms | Balanced (recommended default) |
//! | 100 | 95% | 3ms | High-quality search |
//! | 200 | 98% | 6ms | Offline batch processing |

use crate::index::diskann::graph::VamanaGraph;
use crate::types::error::Result;
use std::collections::{BinaryHeap, HashSet};

/// Parameters for search operation.
#[derive(Debug, Clone)]
pub struct SearchParams {
    /// Number of results to return (k)
    pub top_k: usize,

    /// Search list size (beam width, L parameter)
    ///
    /// Larger values improve recall but increase search time.
    /// Recommended: 75-100 for most use cases.
    pub search_list_size: usize,
}

impl Default for SearchParams {
    fn default() -> Self {
        Self {
            top_k: 10,
            search_list_size: 75,
        }
    }
}

/// Greedy beam search for k-nearest neighbors.
///
/// # Arguments
///
/// * `graph` - DiskANN graph structure
/// * `vectors` - All vectors in the dataset
/// * `query` - Query vector
/// * `medoid` - Entry point for search
/// * `params` - Search parameters (k, L)
///
/// # Returns
///
/// Vector of (node_id, distance) pairs, sorted by distance (closest first)
///
/// # Errors
///
/// Returns error if:
/// - Query dimension mismatches vector dimension
/// - Medoid is invalid
///
/// # Example
///
/// ```rust,ignore
/// let params = SearchParams::default();
/// let results = greedy_search(&graph, &vectors, &query, medoid, params)?;
/// assert_eq!(results.len(), params.top_k);
/// ```
pub fn greedy_search(
    graph: &VamanaGraph,
    vectors: &[Vec<f32>],
    query: &[f32],
    medoid: u32,
    params: SearchParams,
) -> Result<Vec<(u32, f32)>> {
    todo!("Implement greedy beam search")
}

/// Priority queue element for search.
///
/// Stores (node_id, distance) with reverse ordering (min-heap).
#[derive(Debug, Clone, PartialEq)]
struct SearchCandidate {
    node_id: u32,
    distance: f32,
}

impl Eq for SearchCandidate {}

impl PartialOrd for SearchCandidate {
    fn partial_cmp(&self, other: &Self) -> Option<std::cmp::Ordering> {
        // Reverse ordering for min-heap (smaller distance = higher priority)
        other.distance.partial_cmp(&self.distance)
    }
}

impl Ord for SearchCandidate {
    fn cmp(&self, other: &Self) -> std::cmp::Ordering {
        self.partial_cmp(other).unwrap_or(std::cmp::Ordering::Equal)
    }
}

/// Maintain a bounded priority queue (keeps only L best candidates).
///
/// Used for beam search candidate list.
struct BoundedPriorityQueue {
    heap: BinaryHeap<SearchCandidate>,
    max_size: usize,
}

impl BoundedPriorityQueue {
    /// Create a new bounded queue.
    ///
    /// # Arguments
    ///
    /// * `max_size` - Maximum number of elements to keep
    fn new(max_size: usize) -> Self {
        Self {
            heap: BinaryHeap::new(),
            max_size,
        }
    }

    /// Insert a candidate, evicting farthest if size exceeded.
    ///
    /// # Arguments
    ///
    /// * `candidate` - Candidate to insert
    ///
    /// # Returns
    ///
    /// `true` if candidate was added, `false` if rejected
    fn push(&mut self, candidate: SearchCandidate) -> bool {
        todo!("Insert with eviction")
    }

    /// Remove and return the closest candidate.
    ///
    /// # Returns
    ///
    /// Closest candidate, or None if queue is empty
    fn pop(&mut self) -> Option<SearchCandidate> {
        self.heap.pop()
    }

    /// Check if queue is empty.
    fn is_empty(&self) -> bool {
        self.heap.is_empty()
    }

    /// Get current size.
    fn len(&self) -> usize {
        self.heap.len()
    }

    /// Convert to sorted vector (closest first).
    fn into_sorted_vec(self) -> Vec<SearchCandidate> {
        let mut vec: Vec<_> = self.heap.into_vec();
        vec.sort_by(|a, b| {
            a.distance
                .partial_cmp(&b.distance)
                .unwrap_or(std::cmp::Ordering::Equal)
        });
        vec
    }
}

/// Compute L2 distance between query and vector.
///
/// # Arguments
///
/// * `query` - Query vector
/// * `vector` - Target vector
///
/// # Returns
///
/// Euclidean distance
///
/// # Panics
///
/// Panics if dimensions mismatch
fn compute_distance(query: &[f32], vector: &[f32]) -> f32 {
    debug_assert_eq!(query.len(), vector.len());
    query
        .iter()
        .zip(vector.iter())
        .map(|(a, b)| (a - b) * (a - b))
        .sum::<f32>()
        .sqrt()
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_search_candidate_ordering() {
        todo!("Test min-heap ordering (smaller distance = higher priority)")
    }

    #[test]
    fn test_bounded_priority_queue() {
        todo!("Test bounded queue eviction")
    }

    #[test]
    fn test_greedy_search_small_graph() {
        todo!("Test search on small graph")
    }

    #[test]
    fn test_greedy_search_finds_neighbors() {
        todo!("Test that search returns actual nearest neighbors")
    }

    #[test]
    fn test_search_list_size_effect() {
        todo!("Test that larger L improves recall")
    }
}
