//! DiskANN vector index implementation.
//!
//! DiskANN (Disk-based Approximate Nearest Neighbor) is a graph-based ANN algorithm
//! optimized for SSD storage and billion-scale datasets. Key innovations:
//!
//! 1. **Vamana graph**: Degree-bounded graph with robust pruning for quality
//! 2. **Memory-mapped I/O**: Zero-copy disk access for massive datasets
//! 3. **Greedy search**: Single-layer beam search (vs HNSW's multi-layer)
//! 4. **Diversity-aware pruning**: Better graph connectivity than pure nearest neighbors
//!
//! # Algorithm Overview
//!
//! **Build phase** (Vamana algorithm):
//! ```text
//! 1. Initialize random graph (each node connects to R random neighbors)
//! 2. For each vertex v (in random order):
//!    a. Greedy search to find candidate neighbors
//!    b. Robust prune: select diverse, high-quality neighbors
//!    c. Add reverse edges to maintain connectivity
//! 3. Compute medoid (most central point for search entry)
//! ```
//!
//! **Search phase**:
//! ```text
//! 1. Start from medoid (global entry point)
//! 2. Greedy beam search:
//!    - Maintain priority queue of size L (search list)
//!    - Expand closest unvisited neighbors
//!    - Terminate when no improvement
//! 3. Return top-k results
//! ```
//!
//! # Performance Characteristics
//!
//! | Metric | Value | Notes |
//! |--------|-------|-------|
//! | Build time | O(n * R * L * d) | n=vectors, R=degree, L=search list, d=dims |
//! | Search time | O(L * d) | Independent of dataset size (graph property) |
//! | Memory (build) | O(n * R * 4 bytes) | Graph structure only |
//! | Memory (search) | O(L + R) | Beam + neighbors (tiny!) |
//! | Disk usage | O(n * (R*4 + d*4)) | Graph + vectors (or compressed) |
//!
//! # Example
//!
//! ```rust,ignore
//! use percolate_rocks::index::diskann::{DiskANNIndex, BuildParams};
//!
//! // Build index from vectors
//! let vectors = vec![vec![0.1, 0.2, 0.3], vec![0.4, 0.5, 0.6]];
//! let params = BuildParams {
//!     max_degree: 64,
//!     alpha: 1.2,
//!     search_list_size: 100,
//! };
//! let index = DiskANNIndex::build(&vectors, params)?;
//!
//! // Search
//! let query = vec![0.2, 0.3, 0.4];
//! let results = index.search(&query, 10, 75)?;
//!
//! // Save to disk (memory-mapped format)
//! index.save("index.diskann")?;
//!
//! // Load from disk (zero-copy)
//! let index = DiskANNIndex::load("index.diskann")?;
//! ```

mod builder;
mod graph;
mod mmap;
mod prune;
mod search;

pub use builder::{build_index, BuildParams};
pub use graph::VamanaGraph;
pub use mmap::{DiskFormat, MmapIndex};
pub use prune::robust_prune;
pub use search::{greedy_search, SearchParams};

use crate::types::error::Result;

/// DiskANN index with Vamana graph structure.
///
/// Supports both in-memory and memory-mapped operation modes.
#[derive(Debug)]
pub struct DiskANNIndex {
    /// Graph structure (adjacency lists)
    graph: VamanaGraph,

    /// Entry point for search (most central node)
    medoid: u32,

    /// Maximum out-degree per vertex
    max_degree: usize,

    /// Vector dimensionality
    dim: usize,
}

impl DiskANNIndex {
    /// Build a new DiskANN index from vectors.
    ///
    /// Uses the Vamana algorithm to construct a high-quality graph.
    ///
    /// # Arguments
    ///
    /// * `vectors` - Dense vectors (all same dimensionality)
    /// * `params` - Build parameters (degree, alpha, search list size)
    ///
    /// # Returns
    ///
    /// Constructed index ready for search
    ///
    /// # Errors
    ///
    /// Returns error if vectors are empty or have inconsistent dimensions
    ///
    /// # Example
    ///
    /// ```rust,ignore
    /// let params = BuildParams::default();
    /// let index = DiskANNIndex::build(&vectors, params)?;
    /// ```
    pub fn build(vectors: &[Vec<f32>], params: BuildParams) -> Result<Self> {
        todo!("Implement Vamana graph construction")
    }

    /// Search for k-nearest neighbors.
    ///
    /// Uses greedy beam search starting from the medoid.
    ///
    /// # Arguments
    ///
    /// * `query` - Query vector (must match index dimensionality)
    /// * `k` - Number of results to return
    /// * `search_list_size` - Beam width (higher = better recall, slower)
    ///
    /// # Returns
    ///
    /// Vector of (node_id, distance) pairs, sorted by distance
    ///
    /// # Errors
    ///
    /// Returns error if query dimension mismatches index
    ///
    /// # Example
    ///
    /// ```rust,ignore
    /// let results = index.search(&query, 10, 75)?;
    /// assert_eq!(results.len(), 10);
    /// ```
    pub fn search(&self, query: &[f32], k: usize, search_list_size: usize) -> Result<Vec<(u32, f32)>> {
        todo!("Implement greedy beam search")
    }

    /// Save index to disk in memory-mapped format.
    ///
    /// # Arguments
    ///
    /// * `path` - Output file path
    ///
    /// # Errors
    ///
    /// Returns error if file I/O fails
    pub fn save(&self, path: &str) -> Result<()> {
        todo!("Serialize to disk-friendly format")
    }

    /// Load index from disk with memory mapping.
    ///
    /// Uses zero-copy access for efficient large-scale search.
    ///
    /// # Arguments
    ///
    /// * `path` - Index file path
    ///
    /// # Returns
    ///
    /// Memory-mapped index ready for search
    ///
    /// # Errors
    ///
    /// Returns error if file not found or corrupted
    pub fn load(path: &str) -> Result<Self> {
        todo!("Load from memory-mapped file")
    }

    /// Get index statistics.
    ///
    /// # Returns
    ///
    /// Statistics including node count, edge count, avg degree
    pub fn stats(&self) -> IndexStats {
        todo!("Compute index statistics")
    }
}

/// Index statistics for monitoring and debugging.
#[derive(Debug, Clone)]
pub struct IndexStats {
    /// Total number of nodes
    pub num_nodes: usize,

    /// Total number of edges
    pub num_edges: usize,

    /// Average out-degree
    pub avg_degree: f64,

    /// Maximum out-degree
    pub max_degree: usize,

    /// Medoid node ID
    pub medoid: u32,
}

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

    #[test]
    fn test_build_small_index() {
        // Build on small dataset
        todo!("Test basic index construction")
    }

    #[test]
    fn test_search_returns_neighbors() {
        // Verify search returns valid results
        todo!("Test search functionality")
    }

    #[test]
    fn test_save_load_roundtrip() {
        // Save and load should preserve index
        todo!("Test serialization")
    }
}
