Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'

vendor/ruvector/examples/dna/src/kmer.rs

@@ -0,0 +1,511 @@
+//! K-mer encoding and HNSW vector indexing for DNA sequences
+//!
+//! This module provides efficient k-mer based vector encoding for DNA sequences
+//! with HNSW indexing for fast similarity search. Implements both k-mer frequency
+//! vectors and MinHash sketching (Mash/sourmash algorithm).
+
+use ruvector_core::{
+    types::{DbOptions, DistanceMetric, HnswConfig, QuantizationConfig, SearchQuery},
+    VectorDB, VectorEntry,
+};
+use std::collections::HashMap;
+use thiserror::Error;
+
+#[derive(Error, Debug)]
+pub enum KmerError {
+    #[error("Invalid k-mer length: {0}")]
+    InvalidKmerLength(usize),
+    #[error("Invalid DNA sequence: {0}")]
+    InvalidSequence(String),
+    #[error("Database error: {0}")]
+    DatabaseError(#[from] ruvector_core::RuvectorError),
+    #[error("Empty sequence")]
+    EmptySequence,
+}
+
+type Result<T> = std::result::Result<T, KmerError>;
+
+/// Nucleotide to 2-bit encoding: A=0, C=1, G=2, T=3
+#[inline]
+fn nucleotide_to_bits(nuc: u8) -> Option<u8> {
+    match nuc.to_ascii_uppercase() {
+        b'A' => Some(0),
+        b'C' => Some(1),
+        b'G' => Some(2),
+        b'T' | b'U' => Some(3),
+        _ => None,
+    }
+}
+
+/// Returns the reverse complement of a DNA sequence
+fn reverse_complement(seq: &[u8]) -> Vec<u8> {
+    seq.iter()
+        .rev()
+        .map(|&nuc| match nuc.to_ascii_uppercase() {
+            b'A' => b'T',
+            b'T' | b'U' => b'A',
+            b'C' => b'G',
+            b'G' => b'C',
+            n => n,
+        })
+        .collect()
+}
+
+/// Returns the canonical k-mer (lexicographically smaller of k-mer and its reverse complement)
+pub fn canonical_kmer(kmer: &[u8]) -> Vec<u8> {
+    let rc = reverse_complement(kmer);
+    if kmer <= rc.as_slice() {
+        kmer.to_vec()
+    } else {
+        rc
+    }
+}
+
+/// K-mer encoder that converts DNA sequences into frequency vectors
+pub struct KmerEncoder {
+    k: usize,
+    dimensions: usize,
+}
+
+impl KmerEncoder {
+    /// Create a new k-mer encoder for k-mers of length k
+    ///
+    /// # Arguments
+    /// * `k` - Length of k-mers (typical values: 21, 31)
+    ///
+    /// Uses feature hashing to limit dimensionality for large k
+    pub fn new(k: usize) -> Result<Self> {
+        if k == 0 || k > 32 {
+            return Err(KmerError::InvalidKmerLength(k));
+        }
+
+        // Calculate dimensions: min(4^k, 1024) using feature hashing
+        let max_kmers = 4_usize.saturating_pow(k as u32);
+        let dimensions = max_kmers.min(1024);
+
+        Ok(Self { k, dimensions })
+    }
+
+    /// Get the number of dimensions in the encoded vector
+    pub fn dimensions(&self) -> usize {
+        self.dimensions
+    }
+
+    /// Encode a DNA sequence into a k-mer frequency vector
+    ///
+    /// Uses canonical k-mer hashing (min of forward/reverse-complement hash)
+    /// to count strand-agnostic k-mers, then normalizes to unit vector.
+    pub fn encode_sequence(&self, seq: &[u8]) -> Result<Vec<f32>> {
+        if seq.len() < self.k {
+            return Err(KmerError::EmptySequence);
+        }
+
+        let mut counts = vec![0u32; self.dimensions];
+        let mut total = 0u32;
+
+        // Extract all k-mers using a sliding window
+        // Avoid Vec allocation by hashing both strands and taking min
+        for window in seq.windows(self.k) {
+            let fwd_hash = Self::fnv1a_hash(window);
+            let rc_hash = Self::fnv1a_hash_rc(window);
+            let canonical_hash = fwd_hash.min(rc_hash);
+            let index = canonical_hash % self.dimensions;
+
+            counts[index] = counts[index].saturating_add(1);
+            total = total.saturating_add(1);
+        }
+
+        // Normalize to frequency vector and then to unit vector
+        let inv_total = 1.0 / total as f32;
+        let mut vector: Vec<f32> = counts
+            .iter()
+            .map(|&count| count as f32 * inv_total)
+            .collect();
+
+        // L2 normalization
+        let norm: f32 = vector.iter().map(|x| x * x).sum::<f32>().sqrt();
+        if norm > 0.0 {
+            let inv_norm = 1.0 / norm;
+            vector.iter_mut().for_each(|x| *x *= inv_norm);
+        }
+
+        Ok(vector)
+    }
+
+    /// FNV-1a hash of a byte slice
+    #[inline]
+    fn fnv1a_hash(data: &[u8]) -> usize {
+        const FNV_OFFSET: u64 = 14695981039346656037;
+        const FNV_PRIME: u64 = 1099511628211;
+        let mut hash = FNV_OFFSET;
+        for &byte in data {
+            hash ^= byte as u64;
+            hash = hash.wrapping_mul(FNV_PRIME);
+        }
+        hash as usize
+    }
+
+    /// FNV-1a hash of reverse complement (avoids Vec allocation)
+    #[inline]
+    fn fnv1a_hash_rc(data: &[u8]) -> usize {
+        const FNV_OFFSET: u64 = 14695981039346656037;
+        const FNV_PRIME: u64 = 1099511628211;
+        let mut hash = FNV_OFFSET;
+        for &byte in data.iter().rev() {
+            let comp = match byte.to_ascii_uppercase() {
+                b'A' => b'T',
+                b'T' | b'U' => b'A',
+                b'C' => b'G',
+                b'G' => b'C',
+                n => n,
+            };
+            hash ^= comp as u64;
+            hash = hash.wrapping_mul(FNV_PRIME);
+        }
+        hash as usize
+    }
+
+    /// Hash a k-mer to an index using FNV-1a hash
+    fn hash_kmer(&self, kmer: &[u8]) -> usize {
+        Self::fnv1a_hash(kmer)
+    }
+}
+
+/// MinHash sketch for fast sequence similarity (Mash/sourmash algorithm)
+pub struct MinHashSketch {
+    num_hashes: usize,
+    hashes: Vec<u64>,
+}
+
+impl MinHashSketch {
+    /// Create a new MinHash sketch with the given number of hashes
+    ///
+    /// # Arguments
+    /// * `num_hashes` - Number of hash values to keep (typically 1000)
+    pub fn new(num_hashes: usize) -> Self {
+        Self {
+            num_hashes,
+            hashes: Vec::new(),
+        }
+    }
+
+    /// Compute MinHash signature for a DNA sequence
+    pub fn sketch(&mut self, seq: &[u8], k: usize) -> Result<&[u64]> {
+        if seq.len() < k {
+            return Err(KmerError::EmptySequence);
+        }
+
+        let mut all_hashes = Vec::with_capacity(seq.len() - k + 1);
+
+        // Hash all k-mers using dual-hash (no Vec allocation per k-mer)
+        for window in seq.windows(k) {
+            let fwd = Self::hash_kmer_64_slice(window);
+            let rc = Self::hash_kmer_64_rc(window);
+            all_hashes.push(fwd.min(rc));
+        }
+
+        // Sort and keep the smallest num_hashes values
+        all_hashes.sort_unstable();
+        all_hashes.truncate(self.num_hashes);
+        self.hashes = all_hashes;
+
+        Ok(&self.hashes)
+    }
+
+    /// Compute Jaccard distance between two MinHash sketches
+    pub fn jaccard_distance(&self, other: &MinHashSketch) -> f32 {
+        if self.hashes.is_empty() || other.hashes.is_empty() {
+            return 1.0;
+        }
+
+        let mut intersection = 0;
+        let mut i = 0;
+        let mut j = 0;
+
+        // Count intersection using sorted arrays
+        while i < self.hashes.len() && j < other.hashes.len() {
+            if self.hashes[i] == other.hashes[j] {
+                intersection += 1;
+                i += 1;
+                j += 1;
+            } else if self.hashes[i] < other.hashes[j] {
+                i += 1;
+            } else {
+                j += 1;
+            }
+        }
+
+        let union = self.hashes.len() + other.hashes.len() - intersection;
+        if union == 0 {
+            return 0.0;
+        }
+
+        let jaccard_similarity = intersection as f32 / union as f32;
+        1.0 - jaccard_similarity
+    }
+
+    /// Hash a k-mer using MurmurHash3-like algorithm (forward strand)
+    #[inline]
+    fn hash_kmer_64_slice(kmer: &[u8]) -> u64 {
+        const C1: u64 = 0x87c37b91114253d5;
+        const C2: u64 = 0x4cf5ad432745937f;
+        let mut h = 0u64;
+        for &byte in kmer {
+            let mut k = byte as u64;
+            k = k.wrapping_mul(C1);
+            k = k.rotate_left(31);
+            k = k.wrapping_mul(C2);
+            h ^= k;
+            h = h.rotate_left(27);
+            h = h.wrapping_mul(5).wrapping_add(0x52dce729);
+        }
+        h ^ kmer.len() as u64
+    }
+
+    /// Hash reverse complement of a k-mer (no Vec allocation)
+    #[inline]
+    fn hash_kmer_64_rc(kmer: &[u8]) -> u64 {
+        const C1: u64 = 0x87c37b91114253d5;
+        const C2: u64 = 0x4cf5ad432745937f;
+        let mut h = 0u64;
+        for &byte in kmer.iter().rev() {
+            let comp = match byte.to_ascii_uppercase() {
+                b'A' => b'T',
+                b'T' | b'U' => b'A',
+                b'C' => b'G',
+                b'G' => b'C',
+                n => n,
+            };
+            let mut k = comp as u64;
+            k = k.wrapping_mul(C1);
+            k = k.rotate_left(31);
+            k = k.wrapping_mul(C2);
+            h ^= k;
+            h = h.rotate_left(27);
+            h = h.wrapping_mul(5).wrapping_add(0x52dce729);
+        }
+        h ^ kmer.len() as u64
+    }
+
+    /// Get the hashes
+    pub fn hashes(&self) -> &[u64] {
+        &self.hashes
+    }
+}
+
+/// Search result for k-mer index queries
+#[derive(Debug, Clone)]
+pub struct KmerSearchResult {
+    pub id: String,
+    pub score: f32,
+    pub distance: f32,
+}
+
+/// K-mer index wrapping VectorDB for sequence similarity search
+pub struct KmerIndex {
+    db: VectorDB,
+    encoder: KmerEncoder,
+    k: usize,
+}
+
+impl KmerIndex {
+    /// Create a new k-mer index
+    ///
+    /// # Arguments
+    /// * `k` - K-mer length
+    /// * `dimensions` - Vector dimensions (should match encoder dimensions)
+    pub fn new(k: usize, dimensions: usize) -> Result<Self> {
+        let encoder = KmerEncoder::new(k)?;
+
+        // Verify dimensions match
+        if encoder.dimensions() != dimensions {
+            return Err(KmerError::InvalidKmerLength(k));
+        }
+
+        let options = DbOptions {
+            dimensions,
+            distance_metric: DistanceMetric::Cosine,
+            storage_path: format!("./kmer_index_k{}.db", k),
+            hnsw_config: Some(HnswConfig {
+                m: 32,
+                ef_construction: 200,
+                ef_search: 100,
+                max_elements: 1_000_000,
+            }),
+            quantization: Some(QuantizationConfig::Scalar),
+        };
+
+        let db = VectorDB::new(options)?;
+
+        Ok(Self { db, encoder, k })
+    }
+
+    /// Index a single DNA sequence
+    pub fn index_sequence(&self, id: &str, sequence: &[u8]) -> Result<()> {
+        let vector = self.encoder.encode_sequence(sequence)?;
+
+        let entry = VectorEntry {
+            id: Some(id.to_string()),
+            vector,
+            metadata: Some({
+                let mut meta = HashMap::new();
+                meta.insert("length".to_string(), serde_json::json!(sequence.len()));
+                meta.insert("k".to_string(), serde_json::json!(self.k));
+                meta
+            }),
+        };
+
+        self.db.insert(entry)?;
+        Ok(())
+    }
+
+    /// Index multiple sequences in a batch
+    pub fn index_batch(&self, sequences: Vec<(&str, &[u8])>) -> Result<()> {
+        let entries: Result<Vec<VectorEntry>> = sequences
+            .into_iter()
+            .map(|(id, seq)| {
+                let vector = self.encoder.encode_sequence(seq)?;
+                Ok(VectorEntry {
+                    id: Some(id.to_string()),
+                    vector,
+                    metadata: Some({
+                        let mut meta = HashMap::new();
+                        meta.insert("length".to_string(), serde_json::json!(seq.len()));
+                        meta.insert("k".to_string(), serde_json::json!(self.k));
+                        meta
+                    }),
+                })
+            })
+            .collect();
+
+        self.db.insert_batch(entries?)?;
+        Ok(())
+    }
+
+    /// Search for similar sequences
+    pub fn search_similar(&self, query: &[u8], top_k: usize) -> Result<Vec<KmerSearchResult>> {
+        let query_vector = self.encoder.encode_sequence(query)?;
+
+        let search_query = SearchQuery {
+            vector: query_vector,
+            k: top_k,
+            filter: None,
+            ef_search: None,
+        };
+
+        let results = self.db.search(search_query)?;
+
+        Ok(results
+            .into_iter()
+            .map(|r| KmerSearchResult {
+                id: r.id,
+                score: r.score,
+                distance: r.score,
+            })
+            .collect())
+    }
+
+    /// Search for sequences with similarity above a threshold
+    pub fn search_with_threshold(
+        &self,
+        query: &[u8],
+        threshold: f32,
+    ) -> Result<Vec<KmerSearchResult>> {
+        // Search with a larger k to ensure we get all candidates
+        let results = self.search_similar(query, 100)?;
+
+        Ok(results
+            .into_iter()
+            .filter(|r| r.distance <= threshold)
+            .collect())
+    }
+
+    /// Get the k-mer length
+    pub fn k(&self) -> usize {
+        self.k
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_nucleotide_encoding() {
+        assert_eq!(nucleotide_to_bits(b'A'), Some(0));
+        assert_eq!(nucleotide_to_bits(b'C'), Some(1));
+        assert_eq!(nucleotide_to_bits(b'G'), Some(2));
+        assert_eq!(nucleotide_to_bits(b'T'), Some(3));
+        assert_eq!(nucleotide_to_bits(b'a'), Some(0));
+        assert_eq!(nucleotide_to_bits(b'N'), None);
+    }
+
+    #[test]
+    fn test_reverse_complement() {
+        let seq = b"ATCG";
+        let rc = reverse_complement(seq);
+        assert_eq!(rc, b"CGAT");
+    }
+
+    #[test]
+    fn test_canonical_kmer() {
+        let kmer1 = b"ATCG";
+        let kmer2 = b"CGAT"; // reverse complement
+
+        let canon1 = canonical_kmer(kmer1);
+        let canon2 = canonical_kmer(kmer2);
+
+        assert_eq!(canon1, canon2);
+    }
+
+    #[test]
+    fn test_kmer_encoder_creation() {
+        let encoder = KmerEncoder::new(3).unwrap();
+        assert_eq!(encoder.k, 3);
+        assert_eq!(encoder.dimensions(), 64);
+    }
+
+    #[test]
+    fn test_kmer_encoder_large_k() {
+        let encoder = KmerEncoder::new(21).unwrap();
+        assert_eq!(encoder.k, 21);
+        assert_eq!(encoder.dimensions(), 1024); // Capped by feature hashing
+    }
+
+    #[test]
+    fn test_encode_sequence() {
+        let encoder = KmerEncoder::new(3).unwrap();
+        let seq = b"ATCGATCG";
+        let vector = encoder.encode_sequence(seq).unwrap();
+
+        assert_eq!(vector.len(), encoder.dimensions());
+
+        // Check L2 normalization
+        let norm: f32 = vector.iter().map(|x| x * x).sum::<f32>().sqrt();
+        assert!((norm - 1.0).abs() < 1e-5);
+    }
+
+    #[test]
+    fn test_minhash_sketch() {
+        let mut sketch = MinHashSketch::new(100);
+        let seq = b"ATCGATCGATCGATCGATCG";
+
+        sketch.sketch(seq, 5).unwrap();
+        assert!(sketch.hashes().len() <= 100);
+    }
+
+    #[test]
+    fn test_jaccard_distance() {
+        let mut sketch1 = MinHashSketch::new(100);
+        let mut sketch2 = MinHashSketch::new(100);
+
+        let seq1 = b"ATCGATCGATCGATCGATCG";
+        let seq2 = b"ATCGATCGATCGATCGATCG"; // Identical
+
+        sketch1.sketch(seq1, 5).unwrap();
+        sketch2.sketch(seq2, 5).unwrap();
+
+        let distance = sketch1.jaccard_distance(&sketch2);
+        assert!(distance < 0.01); // Should be very similar
+    }
+}