oxirs_vec/

index.rs

//! Advanced vector indexing with HNSW and other efficient algorithms

use crate::Vector;

// Re-export VectorIndex trait for use by other modules
pub use crate::VectorIndex;
use anyhow::{anyhow, Result};
use oxirs_core::parallel::*;
use oxirs_core::Triple;
use serde::{Deserialize, Serialize};
use std::cmp::Ordering;
use std::collections::{BinaryHeap, HashMap};
use std::sync::Arc;

use crate::hnsw::{HnswConfig, HnswIndex};
use crate::ivf::{IvfConfig, IvfIndex};
use crate::pq::{PQConfig, PQIndex};

/// Type alias for filter functions
pub type FilterFunction = Box<dyn Fn(&str) -> bool>;
/// Type alias for filter functions with Send + Sync
pub type FilterFunctionSync = Box<dyn Fn(&str) -> bool + Send + Sync>;

/// Configuration for vector index
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub struct IndexConfig {
    /// Index type to use
    pub index_type: IndexType,
    /// Maximum number of connections for each node (for HNSW)
    pub max_connections: usize,
    /// Construction parameter (for HNSW)
    pub ef_construction: usize,
    /// Search parameter (for HNSW)
    pub ef_search: usize,
    /// Distance metric to use
    pub distance_metric: DistanceMetric,
    /// Whether to enable parallel operations
    pub parallel: bool,
}

impl Default for IndexConfig {
    fn default() -> Self {
        Self {
            index_type: IndexType::Hnsw,
            max_connections: 16,
            ef_construction: 200,
            ef_search: 50,
            distance_metric: DistanceMetric::Cosine,
            parallel: true,
        }
    }
}

/// Available index types
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum IndexType {
    /// Hierarchical Navigable Small World
    Hnsw,
    /// Simple flat index (brute force)
    Flat,
    /// IVF (Inverted File) index
    Ivf,
    /// Product Quantization
    PQ,
}

/// Distance metrics supported
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum DistanceMetric {
    /// Cosine distance (1 - cosine_similarity)
    Cosine,
    /// Euclidean (L2) distance
    Euclidean,
    /// Manhattan (L1) distance
    Manhattan,
    /// Dot product (negative for max-heap behavior)
    DotProduct,
}

impl DistanceMetric {
    /// Calculate distance between two vectors
    pub fn distance(&self, a: &[f32], b: &[f32]) -> f32 {
        use oxirs_core::simd::SimdOps;

        match self {
            DistanceMetric::Cosine => f32::cosine_distance(a, b),
            DistanceMetric::Euclidean => f32::euclidean_distance(a, b),
            DistanceMetric::Manhattan => f32::manhattan_distance(a, b),
            DistanceMetric::DotProduct => -f32::dot(a, b), // Negative for max-heap
        }
    }

    /// Calculate distance between two Vector objects
    pub fn distance_vectors(&self, a: &Vector, b: &Vector) -> f32 {
        let a_f32 = a.as_f32();
        let b_f32 = b.as_f32();
        self.distance(&a_f32, &b_f32)
    }
}

/// Search result with distance/score
#[derive(Debug, Clone, PartialEq)]
pub struct SearchResult {
    pub uri: String,
    pub distance: f32,
    pub score: f32,
    pub metadata: Option<HashMap<String, String>>,
}

impl Eq for SearchResult {}

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

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

/// Advanced vector index with multiple implementations
pub struct AdvancedVectorIndex {
    config: IndexConfig,
    vectors: Vec<(String, Vector)>,
    uri_to_id: HashMap<String, usize>,
    hnsw_index: Option<HnswIndex>,
    /// Trained IVF index (populated after `build()` when `IndexType::Ivf`)
    ivf_index: Option<IvfIndex>,
    /// Trained PQ index (populated after `build()` when `IndexType::PQ`)
    pq_index: Option<PQIndex>,
    dimensions: Option<usize>,
}

impl AdvancedVectorIndex {
    pub fn new(config: IndexConfig) -> Self {
        Self {
            config,
            vectors: Vec::new(),
            uri_to_id: HashMap::new(),
            hnsw_index: None,
            ivf_index: None,
            pq_index: None,
            dimensions: None,
        }
    }

    /// Build the index after adding all vectors
    pub fn build(&mut self) -> Result<()> {
        if self.vectors.is_empty() {
            return Ok(());
        }

        match self.config.index_type {
            IndexType::Hnsw => {
                self.build_hnsw_index()?;
            }
            IndexType::Flat => {
                // No special building needed for flat index
            }
            IndexType::Ivf => {
                self.build_ivf_index()?;
            }
            IndexType::PQ => {
                self.build_pq_index()?;
            }
        }

        Ok(())
    }

    fn build_hnsw_index(&mut self) -> Result<()> {
        if self.dimensions.is_some() {
            let hnsw_config = HnswConfig {
                m: self.config.max_connections,
                m_l0: self.config.max_connections * 2,
                ef_construction: self.config.ef_construction,
                ef: self.config.ef_search,
                ..HnswConfig::default()
            };

            let mut hnsw = HnswIndex::new_cpu_only(hnsw_config);

            for (uri, vector) in &self.vectors {
                hnsw.insert(uri.clone(), vector.clone())?;
            }

            self.hnsw_index = Some(hnsw);
        }

        Ok(())
    }

    /// Build an IVF index by training on all stored vectors and then adding them.
    ///
    /// Uses the default `IvfConfig` with `n_clusters` derived from the number of
    /// stored vectors (capped at 256) so that every cluster has at least one
    /// training sample.
    fn build_ivf_index(&mut self) -> Result<()> {
        let training_vectors: Vec<Vector> = self.vectors.iter().map(|(_, v)| v.clone()).collect();
        let n_clusters = (self.vectors.len() / 4).clamp(2, 256);

        let config = IvfConfig {
            n_clusters,
            n_probes: (n_clusters / 8).max(1),
            ..Default::default()
        };
        let mut ivf = IvfIndex::new(config)?;
        ivf.train(&training_vectors)?;

        for (uri, vector) in &self.vectors {
            ivf.insert(uri.clone(), vector.clone())?;
        }

        self.ivf_index = Some(ivf);
        Ok(())
    }

    /// Build a PQ index by training on all stored vectors and then encoding them.
    ///
    /// Chooses `n_subquantizers` so that it evenly divides the vector dimension
    /// and stays within [1, 8].  Falls back gracefully with a descriptive error
    /// if dimensions are not yet known.
    fn build_pq_index(&mut self) -> Result<()> {
        let dims = self
            .dimensions
            .ok_or_else(|| anyhow!("Cannot build PQ index: no vectors have been inserted yet"))?;

        // Pick the largest power-of-two divisor of dims in [1, 8]
        let n_subquantizers = [8usize, 4, 2, 1]
            .iter()
            .copied()
            .find(|&s| dims % s == 0)
            .unwrap_or(1);

        let config = PQConfig {
            n_subquantizers,
            n_centroids: 16, // small for small test sets
            ..Default::default()
        };
        let mut pq = PQIndex::new(config);
        let training_vectors: Vec<Vector> = self.vectors.iter().map(|(_, v)| v.clone()).collect();
        pq.train(&training_vectors)?;

        for (uri, vector) in &self.vectors {
            pq.insert(uri.clone(), vector.clone())?;
        }

        self.pq_index = Some(pq);
        Ok(())
    }

    /// Add metadata to a vector
    pub fn add_metadata(&mut self, _uri: &str, _metadata: HashMap<String, String>) -> Result<()> {
        // For now, we'll store metadata separately
        // In a full implementation, this would be integrated with the index
        Ok(())
    }

    /// Search with advanced parameters
    pub fn search_advanced(
        &self,
        query: &Vector,
        k: usize,
        _ef: Option<usize>,
        filter: Option<FilterFunction>,
    ) -> Result<Vec<SearchResult>> {
        match self.config.index_type {
            IndexType::Hnsw => self.search_hnsw(query, k),
            IndexType::Ivf => self.search_ivf(query, k),
            IndexType::PQ => self.search_pq(query, k),
            IndexType::Flat => self.search_flat(query, k, filter),
        }
    }

    fn search_hnsw(&self, query: &Vector, k: usize) -> Result<Vec<SearchResult>> {
        if let Some(ref hnsw) = self.hnsw_index {
            let results = hnsw.search_knn(query, k)?;

            // `VectorIndex::search_knn` returns similarity (larger = closer);
            // reconstruct a distance-like value for `SearchResult.distance`.
            Ok(results
                .into_iter()
                .map(|(uri, similarity)| SearchResult {
                    uri,
                    distance: 1.0 - similarity,
                    score: similarity,
                    metadata: None,
                })
                .collect())
        } else {
            Err(anyhow!("HNSW index not built"))
        }
    }

    fn search_ivf(&self, query: &Vector, k: usize) -> Result<Vec<SearchResult>> {
        let ivf = self
            .ivf_index
            .as_ref()
            .ok_or_else(|| anyhow!("IVF index not built — call build() first"))?;
        let results = ivf.search_knn(query, k)?;
        // `VectorIndex::search_knn` returns similarity (larger = closer).
        Ok(results
            .into_iter()
            .map(|(uri, similarity)| SearchResult {
                uri,
                distance: 1.0 - similarity,
                score: similarity,
                metadata: None,
            })
            .collect())
    }

    fn search_pq(&self, query: &Vector, k: usize) -> Result<Vec<SearchResult>> {
        let pq = self
            .pq_index
            .as_ref()
            .ok_or_else(|| anyhow!("PQ index not built — call build() first"))?;
        let results = pq.search_knn(query, k)?;
        // `VectorIndex::search_knn` returns similarity (larger = closer).
        Ok(results
            .into_iter()
            .map(|(uri, similarity)| SearchResult {
                uri,
                distance: 1.0 - similarity,
                score: similarity,
                metadata: None,
            })
            .collect())
    }

    fn search_flat(
        &self,
        query: &Vector,
        k: usize,
        filter: Option<FilterFunction>,
    ) -> Result<Vec<SearchResult>> {
        if self.config.parallel && self.vectors.len() > 1000 {
            // For parallel search, we need Send + Sync filter
            if filter.is_some() {
                // Fall back to sequential if filter is present but not Send + Sync
                self.search_flat_sequential(query, k, filter)
            } else {
                self.search_flat_parallel(query, k, None)
            }
        } else {
            self.search_flat_sequential(query, k, filter)
        }
    }

    fn search_flat_sequential(
        &self,
        query: &Vector,
        k: usize,
        filter: Option<FilterFunction>,
    ) -> Result<Vec<SearchResult>> {
        let mut heap = BinaryHeap::new();

        for (uri, vector) in &self.vectors {
            if let Some(ref filter_fn) = filter {
                if !filter_fn(uri) {
                    continue;
                }
            }

            let distance = self.config.distance_metric.distance_vectors(query, vector);

            if heap.len() < k {
                heap.push(std::cmp::Reverse(SearchResult {
                    uri: uri.clone(),
                    distance,
                    score: 1.0 - distance, // Convert distance to similarity score
                    metadata: None,
                }));
            } else if let Some(std::cmp::Reverse(worst)) = heap.peek() {
                if distance < worst.distance {
                    heap.pop();
                    heap.push(std::cmp::Reverse(SearchResult {
                        uri: uri.clone(),
                        distance,
                        score: 1.0 - distance, // Convert distance to similarity score
                        metadata: None,
                    }));
                }
            }
        }

        let mut results: Vec<SearchResult> = heap.into_iter().map(|r| r.0).collect();
        results.sort_by(|a, b| {
            a.distance
                .partial_cmp(&b.distance)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        Ok(results)
    }

    fn search_flat_parallel(
        &self,
        query: &Vector,
        k: usize,
        filter: Option<FilterFunctionSync>,
    ) -> Result<Vec<SearchResult>> {
        // Split vectors into chunks for parallel processing
        let chunk_size = (self.vectors.len() / num_threads()).max(100);

        // Use Arc for thread-safe sharing of the filter
        let filter_arc = filter.map(Arc::new);

        // Process chunks in parallel and collect top-k from each
        let partial_results: Vec<Vec<SearchResult>> = self
            .vectors
            .par_chunks(chunk_size)
            .map(|chunk| {
                let mut local_heap = BinaryHeap::new();
                let filter_ref = filter_arc.as_ref();

                for (uri, vector) in chunk {
                    if let Some(filter_fn) = filter_ref {
                        if !filter_fn(uri) {
                            continue;
                        }
                    }

                    let distance = self.config.distance_metric.distance_vectors(query, vector);

                    if local_heap.len() < k {
                        local_heap.push(std::cmp::Reverse(SearchResult {
                            uri: uri.clone(),
                            distance,
                            score: 1.0 - distance, // Convert distance to similarity score
                            metadata: None,
                        }));
                    } else if let Some(std::cmp::Reverse(worst)) = local_heap.peek() {
                        if distance < worst.distance {
                            local_heap.pop();
                            local_heap.push(std::cmp::Reverse(SearchResult {
                                uri: uri.clone(),
                                distance,
                                score: 1.0 - distance, // Convert distance to similarity score
                                metadata: None,
                            }));
                        }
                    }
                }

                local_heap
                    .into_sorted_vec()
                    .into_iter()
                    .map(|r| r.0)
                    .collect()
            })
            .collect();

        // Merge results from all chunks
        let mut final_heap = BinaryHeap::new();
        for partial in partial_results {
            for result in partial {
                if final_heap.len() < k {
                    final_heap.push(std::cmp::Reverse(result));
                } else if let Some(std::cmp::Reverse(worst)) = final_heap.peek() {
                    if result.distance < worst.distance {
                        final_heap.pop();
                        final_heap.push(std::cmp::Reverse(result));
                    }
                }
            }
        }

        let mut results: Vec<SearchResult> = final_heap.into_iter().map(|r| r.0).collect();
        results.sort_by(|a, b| {
            a.distance
                .partial_cmp(&b.distance)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        Ok(results)
    }

    /// Get index statistics
    pub fn stats(&self) -> IndexStats {
        IndexStats {
            num_vectors: self.vectors.len(),
            dimensions: self.dimensions.unwrap_or(0),
            index_type: self.config.index_type,
            memory_usage: self.estimate_memory_usage(),
        }
    }

    fn estimate_memory_usage(&self) -> usize {
        let vector_memory = self.vectors.len()
            * (std::mem::size_of::<String>()
                + self.dimensions.unwrap_or(0) * std::mem::size_of::<f32>());

        let uri_map_memory =
            self.uri_to_id.len() * (std::mem::size_of::<String>() + std::mem::size_of::<usize>());

        vector_memory + uri_map_memory
    }

    /// Get the number of vectors in the index
    pub fn len(&self) -> usize {
        self.vectors.len()
    }

    /// Check if the index is empty
    pub fn is_empty(&self) -> bool {
        self.vectors.is_empty()
    }

    /// Add a vector with RDF triple and metadata (for compatibility with tests)
    pub fn add(
        &mut self,
        id: String,
        vector: Vec<f32>,
        _triple: Triple,
        _metadata: HashMap<String, String>,
    ) -> Result<()> {
        let vector_obj = Vector::new(vector);
        self.insert(id, vector_obj)
    }

    /// Search for nearest neighbors (for compatibility with tests)
    pub fn search(&self, query: &[f32], k: usize) -> Result<Vec<SearchResult>> {
        let query_vector = Vector::new(query.to_vec());
        let results = self.search_advanced(&query_vector, k, None, None)?;
        Ok(results)
    }
}

impl VectorIndex for AdvancedVectorIndex {
    fn insert(&mut self, uri: String, vector: Vector) -> Result<()> {
        if let Some(dims) = self.dimensions {
            if vector.dimensions != dims {
                return Err(anyhow!(
                    "Vector dimensions ({}) don't match index dimensions ({})",
                    vector.dimensions,
                    dims
                ));
            }
        } else {
            self.dimensions = Some(vector.dimensions);
        }

        let id = self.vectors.len();
        self.uri_to_id.insert(uri.clone(), id);
        self.vectors.push((uri, vector));

        Ok(())
    }

    fn search_knn(&self, query: &Vector, k: usize) -> Result<Vec<(String, f32)>> {
        let results = self.search_advanced(query, k, None, None)?;
        Ok(results.into_iter().map(|r| (r.uri, r.distance)).collect())
    }

    fn search_threshold(&self, query: &Vector, threshold: f32) -> Result<Vec<(String, f32)>> {
        let mut results = Vec::new();

        for (uri, vector) in &self.vectors {
            let distance = self.config.distance_metric.distance_vectors(query, vector);
            if distance <= threshold {
                results.push((uri.clone(), distance));
            }
        }

        results.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(std::cmp::Ordering::Equal));
        Ok(results)
    }

    fn get_vector(&self, uri: &str) -> Option<&Vector> {
        // For AdvancedVectorIndex, vectors are stored in the vectors field
        // regardless of the index type being used
        self.vectors.iter().find(|(u, _)| u == uri).map(|(_, v)| v)
    }
}

/// Index performance statistics
#[derive(Debug, Clone)]
pub struct IndexStats {
    pub num_vectors: usize,
    pub dimensions: usize,
    pub index_type: IndexType,
    pub memory_usage: usize,
}

/// Quantized vector index for memory efficiency
pub struct QuantizedVectorIndex {
    config: IndexConfig,
    quantized_vectors: Vec<Vec<u8>>,
    centroids: Vec<Vector>,
    uri_to_id: HashMap<String, usize>,
    dimensions: Option<usize>,
}

impl QuantizedVectorIndex {
    pub fn new(config: IndexConfig, num_centroids: usize) -> Self {
        Self {
            config,
            quantized_vectors: Vec::new(),
            centroids: Vec::with_capacity(num_centroids),
            uri_to_id: HashMap::new(),
            dimensions: None,
        }
    }

    /// Train quantization centroids using k-means
    pub fn train_quantization(&mut self, training_vectors: &[Vector]) -> Result<()> {
        if training_vectors.is_empty() {
            return Err(anyhow!("No training vectors provided"));
        }

        let dimensions = training_vectors[0].dimensions;
        self.dimensions = Some(dimensions);

        // Simple k-means clustering for centroids
        self.centroids = kmeans_clustering(training_vectors, self.centroids.capacity())?;

        Ok(())
    }

    fn quantize_vector(&self, vector: &Vector) -> Vec<u8> {
        let mut quantized = Vec::new();

        // Find nearest centroid for each dimension chunk
        let chunk_size = vector.dimensions / self.centroids.len().max(1);

        let vector_f32 = vector.as_f32();
        for chunk in vector_f32.chunks(chunk_size) {
            let mut best_centroid = 0u8;
            let mut best_distance = f32::INFINITY;

            for (i, centroid) in self.centroids.iter().enumerate() {
                let centroid_f32 = centroid.as_f32();
                let centroid_chunk = &centroid_f32[0..chunk.len().min(centroid.dimensions)];
                use oxirs_core::simd::SimdOps;
                let distance = f32::euclidean_distance(chunk, centroid_chunk);
                if distance < best_distance {
                    best_distance = distance;
                    best_centroid = i as u8;
                }
            }

            quantized.push(best_centroid);
        }

        quantized
    }
}

impl VectorIndex for QuantizedVectorIndex {
    fn insert(&mut self, uri: String, vector: Vector) -> Result<()> {
        if self.centroids.is_empty() {
            return Err(anyhow!(
                "Quantization not trained. Call train_quantization first."
            ));
        }

        let id = self.quantized_vectors.len();
        self.uri_to_id.insert(uri.clone(), id);

        let quantized = self.quantize_vector(&vector);
        self.quantized_vectors.push(quantized);

        Ok(())
    }

    fn search_knn(&self, query: &Vector, k: usize) -> Result<Vec<(String, f32)>> {
        let query_quantized = self.quantize_vector(query);
        let mut results = Vec::new();

        for (uri, quantized) in self.uri_to_id.keys().zip(&self.quantized_vectors) {
            let distance = hamming_distance(&query_quantized, quantized);
            results.push((uri.clone(), distance));
        }

        results.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(std::cmp::Ordering::Equal));
        results.truncate(k);

        Ok(results)
    }

    fn search_threshold(&self, query: &Vector, threshold: f32) -> Result<Vec<(String, f32)>> {
        let query_quantized = self.quantize_vector(query);
        let mut results = Vec::new();

        for (uri, quantized) in self.uri_to_id.keys().zip(&self.quantized_vectors) {
            let distance = hamming_distance(&query_quantized, quantized);
            if distance <= threshold {
                results.push((uri.clone(), distance));
            }
        }

        results.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(std::cmp::Ordering::Equal));
        Ok(results)
    }

    fn get_vector(&self, _uri: &str) -> Option<&Vector> {
        // Quantized index doesn't store original vectors
        // Return None as we only have quantized representations
        None
    }
}

// Helper functions that don't have SIMD equivalents

fn hamming_distance(a: &[u8], b: &[u8]) -> f32 {
    a.iter().zip(b).filter(|(x, y)| x != y).count() as f32
}

// K-means clustering for quantization
fn kmeans_clustering(vectors: &[Vector], k: usize) -> Result<Vec<Vector>> {
    if vectors.is_empty() || k == 0 {
        return Ok(Vec::new());
    }

    let dimensions = vectors[0].dimensions;
    let mut centroids = Vec::with_capacity(k);

    // Initialize centroids randomly
    for i in 0..k {
        let idx = i % vectors.len();
        centroids.push(vectors[idx].clone());
    }

    // Simple k-means iterations
    for _ in 0..10 {
        let mut clusters: Vec<Vec<&Vector>> = vec![Vec::new(); k];

        // Assign vectors to nearest centroid
        for vector in vectors {
            let mut best_centroid = 0;
            let mut best_distance = f32::INFINITY;

            for (i, centroid) in centroids.iter().enumerate() {
                let vector_f32 = vector.as_f32();
                let centroid_f32 = centroid.as_f32();
                use oxirs_core::simd::SimdOps;
                let distance = f32::euclidean_distance(&vector_f32, &centroid_f32);
                if distance < best_distance {
                    best_distance = distance;
                    best_centroid = i;
                }
            }

            clusters[best_centroid].push(vector);
        }

        // Update centroids
        for (i, cluster) in clusters.iter().enumerate() {
            if !cluster.is_empty() {
                let mut new_centroid = vec![0.0; dimensions];

                for vector in cluster {
                    let vector_f32 = vector.as_f32();
                    for (j, &value) in vector_f32.iter().enumerate() {
                        new_centroid[j] += value;
                    }
                }

                for value in &mut new_centroid {
                    *value /= cluster.len() as f32;
                }

                centroids[i] = Vector::new(new_centroid);
            }
        }
    }

    Ok(centroids)
}

/// Multi-index system that combines multiple index types
pub struct MultiIndex {
    indices: HashMap<String, Box<dyn VectorIndex>>,
    default_index: String,
}

impl MultiIndex {
    pub fn new() -> Self {
        Self {
            indices: HashMap::new(),
            default_index: String::new(),
        }
    }

    pub fn add_index(&mut self, name: String, index: Box<dyn VectorIndex>) {
        if self.indices.is_empty() {
            self.default_index = name.clone();
        }
        self.indices.insert(name, index);
    }

    pub fn set_default(&mut self, name: &str) -> Result<()> {
        if self.indices.contains_key(name) {
            self.default_index = name.to_string();
            Ok(())
        } else {
            Err(anyhow!("Index '{}' not found", name))
        }
    }

    pub fn search_index(
        &self,
        index_name: &str,
        query: &Vector,
        k: usize,
    ) -> Result<Vec<(String, f32)>> {
        if let Some(index) = self.indices.get(index_name) {
            index.search_knn(query, k)
        } else {
            Err(anyhow!("Index '{}' not found", index_name))
        }
    }
}

impl Default for MultiIndex {
    fn default() -> Self {
        Self::new()
    }
}

impl VectorIndex for MultiIndex {
    fn insert(&mut self, uri: String, vector: Vector) -> Result<()> {
        if let Some(index) = self.indices.get_mut(&self.default_index) {
            index.insert(uri, vector)
        } else {
            Err(anyhow!("No default index set"))
        }
    }

    fn search_knn(&self, query: &Vector, k: usize) -> Result<Vec<(String, f32)>> {
        if let Some(index) = self.indices.get(&self.default_index) {
            index.search_knn(query, k)
        } else {
            Err(anyhow!("No default index set"))
        }
    }

    fn search_threshold(&self, query: &Vector, threshold: f32) -> Result<Vec<(String, f32)>> {
        if let Some(index) = self.indices.get(&self.default_index) {
            index.search_threshold(query, threshold)
        } else {
            Err(anyhow!("No default index set"))
        }
    }

    fn get_vector(&self, uri: &str) -> Option<&Vector> {
        if let Some(index) = self.indices.get(&self.default_index) {
            index.get_vector(uri)
        } else {
            None
        }
    }
}

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

    fn sample_vectors() -> Vec<(&'static str, Vector)> {
        vec![
            (
                "http://example.org/a",
                Vector::new(vec![1.0, 0.0, 0.0, 1.0]),
            ),
            (
                "http://example.org/b",
                Vector::new(vec![0.0, 1.0, 1.0, 0.0]),
            ),
            (
                "http://example.org/c",
                Vector::new(vec![-1.0, 0.0, 0.0, -1.0]),
            ),
            (
                "http://example.org/d",
                Vector::new(vec![0.0, -1.0, -1.0, 0.0]),
            ),
            (
                "http://example.org/e",
                Vector::new(vec![0.5, 0.5, 0.5, 0.5]),
            ),
            (
                "http://example.org/f",
                Vector::new(vec![-0.5, 0.5, -0.5, 0.5]),
            ),
            (
                "http://example.org/g",
                Vector::new(vec![1.0, 1.0, 0.0, 0.0]),
            ),
            (
                "http://example.org/h",
                Vector::new(vec![0.0, 0.0, 1.0, 1.0]),
            ),
        ]
    }

    fn build_index(index_type: IndexType) -> Result<AdvancedVectorIndex> {
        let config = IndexConfig {
            index_type,
            ..Default::default()
        };
        let mut idx = AdvancedVectorIndex::new(config);
        for (uri, vec) in sample_vectors() {
            idx.insert(uri.to_string(), vec)?;
        }
        idx.build()?;
        Ok(idx)
    }

    #[test]
    fn test_ivf_build_and_search() -> Result<()> {
        let idx = build_index(IndexType::Ivf)?;
        assert!(idx.ivf_index.is_some(), "IVF index should be built");

        let query = Vector::new(vec![1.0, 0.0, 0.0, 1.0]);
        let results = idx.search(&query.as_f32(), 3)?;
        assert!(!results.is_empty(), "IVF search should return results");
        Ok(())
    }

    #[test]
    fn test_pq_build_and_search() -> Result<()> {
        let idx = build_index(IndexType::PQ)?;
        assert!(idx.pq_index.is_some(), "PQ index should be built");

        let query = Vector::new(vec![0.0, 1.0, 1.0, 0.0]);
        let results = idx.search(&query.as_f32(), 3)?;
        assert!(!results.is_empty(), "PQ search should return results");
        Ok(())
    }

    #[test]
    fn test_flat_search_unchanged() -> Result<()> {
        let idx = build_index(IndexType::Flat)?;
        let query = Vector::new(vec![1.0, 0.0, 0.0, 1.0]);
        let results = idx.search(&query.as_f32(), 2)?;
        assert_eq!(
            results.len(),
            2,
            "Flat search should return exactly k results"
        );
        Ok(())
    }
}