//! Integration tests comparing HNSW, DiskANN, and BM25 search implementations.
//!
//! Tests:
//! 1. Performance benchmarks (latency, throughput)
//! 2. Recall quality (@ k=10, k=100)
//! 3. Memory footprint
//! 4. Hybrid search (vector + keyword fusion)

use percolate_rocks::index::{BM25Index, DiskANNIndex, HnswIndex};
use percolate_rocks::storage::Storage;

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

    #[test]
    fn test_hnsw_vs_diskann_build_time() {
        // Compare build times for 10k, 100k, 1M vectors
        todo!("Benchmark: HNSW vs DiskANN build time")
    }

    #[test]
    fn test_hnsw_vs_diskann_search_latency() {
        // Compare search latency (p50, p95, p99)
        todo!("Benchmark: HNSW vs DiskANN search latency")
    }

    #[test]
    fn test_hnsw_vs_diskann_recall() {
        // Compare recall@10, recall@100 on test dataset
        todo!("Benchmark: HNSW vs DiskANN recall quality")
    }

    #[test]
    fn test_hnsw_vs_diskann_memory() {
        // Compare memory footprint (RSS, heap, mmap)
        todo!("Benchmark: HNSW vs DiskANN memory usage")
    }

    #[test]
    fn test_diskann_mmap_vs_in_memory() {
        // Compare memory-mapped vs in-memory DiskANN
        todo!("Benchmark: DiskANN mmap vs in-memory")
    }
}

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

    #[test]
    fn test_bm25_build_index() {
        // Build BM25 index from documents
        todo!("Test: BM25 index construction")
    }

    #[test]
    fn test_bm25_search_single_term() {
        // Search for single term, verify ranking
        todo!("Test: BM25 single-term search")
    }

    #[test]
    fn test_bm25_search_multi_term() {
        // Search for multiple terms, verify score aggregation
        todo!("Test: BM25 multi-term search")
    }

    #[test]
    fn test_bm25_ranking_quality() {
        // Verify that more relevant docs rank higher
        todo!("Test: BM25 ranking quality")
    }

    #[test]
    fn test_bm25_parameter_tuning() {
        // Compare different k1, b parameters
        todo!("Test: BM25 parameter sensitivity")
    }
}

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

    #[test]
    fn test_vector_keyword_fusion() {
        // Combine vector (semantic) + BM25 (keyword) scores
        todo!("Test: Hybrid search with score fusion")
    }

    #[test]
    fn test_keyword_prefilter_vector() {
        // Use BM25 to filter candidates, then vector rerank
        todo!("Test: BM25 pre-filter → vector rerank")
    }

    #[test]
    fn test_vector_prefilter_keyword() {
        // Use vector to filter candidates, then BM25 rerank
        todo!("Test: Vector pre-filter → BM25 rerank")
    }

    #[test]
    fn test_rrf_fusion() {
        // Reciprocal Rank Fusion (RRF) for combining results
        todo!("Test: RRF fusion algorithm")
    }

    #[test]
    fn test_weighted_fusion() {
        // Weighted linear combination of scores
        todo!("Test: Weighted score fusion")
    }
}

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

    #[test]
    fn test_hnsw_10k_vectors() {
        // HNSW on 10k vectors (small dataset)
        todo!("Scalability: HNSW 10k vectors")
    }

    #[test]
    fn test_hnsw_100k_vectors() {
        // HNSW on 100k vectors (medium dataset)
        todo!("Scalability: HNSW 100k vectors")
    }

    #[test]
    fn test_diskann_100k_vectors() {
        // DiskANN on 100k vectors (medium dataset)
        todo!("Scalability: DiskANN 100k vectors")
    }

    #[test]
    fn test_diskann_1m_vectors() {
        // DiskANN on 1M vectors (large dataset)
        todo!("Scalability: DiskANN 1M vectors")
    }

    #[test]
    fn test_bm25_100k_docs() {
        // BM25 on 100k documents
        todo!("Scalability: BM25 100k documents")
    }

    #[test]
    fn test_bm25_1m_docs() {
        // BM25 on 1M documents
        todo!("Scalability: BM25 1M documents")
    }
}

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

    #[test]
    fn test_hnsw_save_load() {
        // Test HNSW serialization to RocksDB
        todo!("Test: HNSW save/load roundtrip")
    }

    #[test]
    fn test_diskann_save_load() {
        // Test DiskANN serialization to mmap file
        todo!("Test: DiskANN save/load roundtrip")
    }

    #[test]
    fn test_bm25_save_load() {
        // Test BM25 serialization to RocksDB
        todo!("Test: BM25 save/load roundtrip")
    }

    #[test]
    fn test_all_indexes_persistence() {
        // Save all three indexes, reload, verify correctness
        todo!("Test: All indexes persistence")
    }
}

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

    #[test]
    fn test_hnsw_exact_neighbors() {
        // Verify HNSW returns approximate nearest neighbors
        todo!("Test: HNSW correctness (recall >= 95%)")
    }

    #[test]
    fn test_diskann_exact_neighbors() {
        // Verify DiskANN returns approximate nearest neighbors
        todo!("Test: DiskANN correctness (recall >= 95%)")
    }

    #[test]
    fn test_bm25_term_matching() {
        // Verify BM25 returns docs with query terms
        todo!("Test: BM25 term matching correctness")
    }

    #[test]
    fn test_bm25_score_monotonicity() {
        // Verify BM25 scores are monotonic (more matches = higher score)
        todo!("Test: BM25 score monotonicity")
    }
}

/// Helper function to generate random vectors for testing.
fn generate_random_vectors(count: usize, dim: usize) -> Vec<Vec<f32>> {
    todo!("Generate random f32 vectors")
}

/// Helper function to generate clustered vectors (for recall testing).
fn generate_clustered_vectors(num_clusters: usize, points_per_cluster: usize, dim: usize) -> Vec<Vec<f32>> {
    todo!("Generate clustered vectors for recall testing")
}

/// Helper function to compute exact k-NN (brute force, for ground truth).
fn exact_knn(query: &[f32], vectors: &[Vec<f32>], k: usize) -> Vec<(usize, f32)> {
    todo!("Brute-force k-NN for ground truth")
}

/// Helper function to compute recall@k.
///
/// recall@k = |intersection(predicted, true_top_k)| / k
fn compute_recall(predicted: &[(usize, f32)], ground_truth: &[(usize, f32)], k: usize) -> f64 {
    todo!("Compute recall@k metric")
}

/// Helper function to generate test documents for BM25.
fn generate_test_documents(count: usize) -> Vec<String> {
    todo!("Generate synthetic text documents")
}
