motedb 0.1.4

//! Text Index Base Types
//!
//! Provides shared types for text indexing:
//! - Tokenizers (Whitespace, Ngram)
//! - PostingList with Roaring Bitmap
//! - BM25 Configuration
//! - Core data structures

use crate::{Result, StorageError};
use roaring::RoaringBitmap;
use std::collections::HashMap;
use serde::{Serialize, Deserialize};

/// Term ID (32-bit, supports 4B unique tokens)
pub type TermId = u32;

/// Document ID (64-bit)
pub type DocId = u64;

/// Token position in document
pub type Position = u32;

//=============================================================================
// PART 1: Pluggable Tokenizer System
//=============================================================================

/// Token produced by tokenizer
#[derive(Debug, Clone)]
pub struct Token {
    pub text: String,
    pub position: Position,
}

/// Tokenizer trait for pluggable text analysis
pub trait Tokenizer: Send + Sync {
    /// Tokenize text into a list of tokens
    fn tokenize(&self, text: &str) -> Vec<Token>;
    
    /// Get tokenizer name
    fn name(&self) -> &str;
}

/// Whitespace tokenizer (default, fast)
#[derive(Debug, Clone)]
pub struct WhitespaceTokenizer {
    pub case_sensitive: bool,
    pub min_len: usize,
    pub max_len: usize,
}

impl Default for WhitespaceTokenizer {
    fn default() -> Self {
        Self {
            case_sensitive: false,
            min_len: 1,
            max_len: 64,
        }
    }
}

impl Tokenizer for WhitespaceTokenizer {
    fn tokenize(&self, text: &str) -> Vec<Token> {
        let normalized = if self.case_sensitive {
            text.to_string()
        } else {
            text.to_lowercase()
        };
        
        normalized
            .split(|c: char| !c.is_alphanumeric() && c != '_')
            .enumerate()
            .filter_map(|(i, s)| {
                if s.len() >= self.min_len && s.len() <= self.max_len {
                    Some(Token {
                        text: s.to_string(),
                        position: i as Position,
                    })
                } else {
                    None
                }
            })
            .collect()
    }
    
    fn name(&self) -> &str {
        "whitespace"
    }
}

/// N-gram tokenizer (for CJK and fuzzy search)
#[derive(Debug, Clone)]
pub struct NgramTokenizer {
    pub n: usize,
    pub case_sensitive: bool,
}

impl NgramTokenizer {
    pub fn new(n: usize) -> Self {
        Self {
            n,
            case_sensitive: false,
        }
    }
}

impl Tokenizer for NgramTokenizer {
    fn tokenize(&self, text: &str) -> Vec<Token> {
        let normalized = if self.case_sensitive {
            text.to_string()
        } else {
            text.to_lowercase()
        };
        
        let chars: Vec<char> = normalized.chars().collect();
        if chars.len() < self.n {
            return vec![];
        }
        
        chars
            .windows(self.n)
            .enumerate()
            .map(|(i, window)| Token {
                text: window.iter().collect(),
                position: i as Position,
            })
            .collect()
    }
    
    fn name(&self) -> &str {
        "ngram"
    }
}

//=============================================================================
// PART 2: BM25 Ranking
//=============================================================================

/// BM25 scoring parameters
#[derive(Debug, Clone, Copy)]
pub struct BM25Config {
    /// Term frequency saturation parameter (typically 1.2-2.0)
    pub k1: f32,
    
    /// Length normalization parameter (typically 0.75)
    pub b: f32,
}

impl Default for BM25Config {
    fn default() -> Self {
        Self {
            k1: 1.5,
            b: 0.75,
        }
    }
}

//=============================================================================
// PART 3: Core Data Structures
//=============================================================================

/// Posting list for a term (memory-optimized)
#[derive(Debug, Clone)]
pub struct PostingList {
    /// Document IDs (Roaring Bitmap for 90%+ compression)
    doc_ids: RoaringBitmap,
    
    /// Document frequencies (parallel array to doc_ids for memory efficiency)
    /// doc_freqs[i] is the frequency of term in doc_ids[i]
    /// ✅ This saves ~50% memory vs HashMap<DocId, u16>
    doc_freqs: Vec<u16>,
    
    /// Positions in documents (for phrase queries, disabled by default)
    positions: Option<HashMap<DocId, Vec<Position>>>,
}

// Manual Serialize/Deserialize for PostingList
impl Serialize for PostingList {
    fn serialize<S>(&self, serializer: S) -> std::result::Result<S::Ok, S::Error>
    where
        S: serde::Serializer,
    {
        use serde::ser::SerializeStruct;
        let mut state = serializer.serialize_struct("PostingList", 3)?;
        
        // Serialize roaring bitmap as vec of u32
        let doc_ids: Vec<u32> = self.doc_ids.iter().collect();
        state.serialize_field("doc_ids", &doc_ids)?;
        state.serialize_field("doc_freqs", &self.doc_freqs)?;
        state.serialize_field("positions", &self.positions)?;
        state.end()
    }
}

impl<'de> Deserialize<'de> for PostingList {
    fn deserialize<D>(deserializer: D) -> std::result::Result<Self, D::Error>
    where
        D: serde::Deserializer<'de>,
    {
        #[derive(Deserialize)]
        struct Helper {
            doc_ids: Vec<u32>,
            doc_freqs: Vec<u16>,
            positions: Option<HashMap<DocId, Vec<Position>>>,
        }
        
        let helper = Helper::deserialize(deserializer)?;
        let doc_ids = RoaringBitmap::from_iter(helper.doc_ids);
        
        Ok(PostingList {
            doc_ids,
            doc_freqs: helper.doc_freqs,
            positions: helper.positions,
        })
    }
}

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

impl PostingList {
    pub fn new() -> Self {
        Self {
            doc_ids: RoaringBitmap::new(),
            doc_freqs: Vec::new(),
            positions: Some(HashMap::new()),
        }
    }
    
    /// Create PostingList without positions map (memory optimization)
    /// 
    /// When positions are disabled, we don't need the HashMap at all.
    /// This saves ~50% memory and eliminates HashMap lookup overhead!
    pub fn new_without_positions(disable_positions: bool) -> Self {
        Self {
            doc_ids: RoaringBitmap::new(),
            doc_freqs: Vec::new(),
            positions: if disable_positions { None } else { Some(HashMap::new()) },
        }
    }
    
    /// Compact serialization for disk persistence (85% space saving)
    /// 
    /// Format:
    /// - [roaring_bitmap_bytes] (variable, ~2-4KB for 2000 docs)
    /// - [doc_freqs_count: u32] (4 bytes)
    /// - [doc_freqs: u16...] (2 * count bytes)
    /// 
    /// Total: ~6-8KB (vs 50-70KB with bincode)
    pub fn serialize_compact(&self) -> Result<Vec<u8>> {
        let mut buf = Vec::new();
        
        // 1. Serialize RoaringBitmap (highly compressed)
        self.doc_ids.serialize_into(&mut buf)
            .map_err(|e| StorageError::Serialization(format!("RoaringBitmap serialize error: {}", e)))?;
        
        // 2. Calculate and serialize doc_freqs from positions
        let doc_freqs: Vec<u16> = if let Some(ref pos_map) = self.positions {
            self.doc_ids.iter()
                .map(|id| pos_map.get(&(id as u64)).map(|v| v.len() as u16).unwrap_or(1))
                .collect()
        } else {
            // No positions tracked, assume frequency=1 for all docs
            vec![1u16; self.doc_ids.len() as usize]
        };
        
        buf.extend_from_slice(&(doc_freqs.len() as u32).to_le_bytes());
        for &freq in &doc_freqs {
            buf.extend_from_slice(&freq.to_le_bytes());
        }
        
        // Note: positions are not serialized (stored separately if needed)
        
        Ok(buf)
    }
    
    /// Deserialize from compact format
    pub fn deserialize_compact(buf: &[u8]) -> Result<Self> {
        use std::io::Cursor;
        
        if buf.is_empty() {
            return Err(StorageError::InvalidData("Empty buffer".into()));
        }
        
        let mut cursor = Cursor::new(buf);
        
        // 1. Deserialize RoaringBitmap
        let doc_ids = RoaringBitmap::deserialize_from(&mut cursor)
            .map_err(|e| StorageError::Serialization(
                format!("RoaringBitmap deserialize error (buf_len={}): {}", buf.len(), e)
            ))?;
        
        let offset = cursor.position() as usize;
        
        // 2. Deserialize doc_freqs
        if offset + 4 > buf.len() {
            return Err(StorageError::InvalidData(
                format!("Buffer too small for doc_freqs count: offset={}, buf_len={}", offset, buf.len())
            ));
        }
        
        let count = u32::from_le_bytes([
            buf[offset], buf[offset+1], buf[offset+2], buf[offset+3]
        ]) as usize;
        
        let mut offset = offset + 4;
        let mut doc_freqs = Vec::with_capacity(count);
        
        for _ in 0..count {
            if offset + 2 > buf.len() {
                return Err(StorageError::InvalidData("Buffer too small for doc_freqs".into()));
            }
            
            let freq = u16::from_le_bytes([buf[offset], buf[offset+1]]);
            doc_freqs.push(freq);
            offset += 2;
        }
        
        Ok(PostingList {
            doc_ids,
            doc_freqs,
            positions: None,  // Positions not stored in compact format
        })
    }
    
    /// Add a document occurrence (optimized for sequential inserts)
    pub fn add(&mut self, doc_id: DocId, position: Option<Position>) {
        // Fast path: just insert into Roaring and append to freqs
        // We'll rebuild the parallel structure only when needed (during serialization)
        self.doc_ids.insert(doc_id as u32);
        
        // For positions
        if let Some(pos) = position {
            if let Some(ref mut pos_map) = self.positions {
                pos_map.entry(doc_id).or_default().push(pos);
            }
        }
        
        // Note: doc_freqs will be out of sync until we call rebuild_doc_freqs_array()
        // This is OK because we only rebuild before serialization
    }
    
    /// Add multiple occurrences of a document (for term frequency)
    pub fn add_multiple(&mut self, doc_id: DocId, _count: u16, positions: Option<Vec<Position>>) {
        self.doc_ids.insert(doc_id as u32);
        
        if let Some(pos_vec) = positions {
            if let Some(ref mut pos_map) = self.positions {
                pos_map.entry(doc_id).or_default().extend(pos_vec);
            }
        }
    }
    
    /// Rebuild doc_freqs array after doc_ids change (maintains parallel structure)
    fn rebuild_doc_freqs_array(&mut self) {
        let old_freqs_map: HashMap<u64, u16> = self.doc_ids.iter()
            .zip(self.doc_freqs.iter())
            .map(|(id, &freq)| (id as u64, freq))
            .collect();
        
        self.doc_freqs = self.doc_ids.iter()
            .map(|id| *old_freqs_map.get(&(id as u64)).unwrap_or(&0))
            .collect();
    }
    
    /// Merge another posting list into this one
    pub fn merge(&mut self, other: &PostingList) {
        // Build temporary HashMap for easier merging
        let mut freq_map: HashMap<u64, u16> = self.doc_ids.iter()
            .zip(self.doc_freqs.iter())
            .map(|(id, &freq)| (id as u64, freq))
            .collect();
        
        // Merge other's frequencies
        for (id, &freq) in other.doc_ids.iter().zip(other.doc_freqs.iter()) {
            *freq_map.entry(id as u64).or_insert(0) += freq;
        }
        
        // Merge doc_ids
        self.doc_ids |= &other.doc_ids;
        
        // Rebuild parallel array
        self.doc_freqs = self.doc_ids.iter()
            .map(|id| *freq_map.get(&(id as u64)).unwrap_or(&0))
            .collect();
        
        // Merge positions
        if let (Some(ref mut self_pos), Some(ref other_pos)) = (&mut self.positions, &other.positions) {
            for (doc_id, positions) in other_pos {
                self_pos.entry(*doc_id).or_default().extend(positions);
            }
        }
    }
    
    pub fn doc_ids(&self) -> Vec<DocId> {
        self.doc_ids.iter().map(|id| id as DocId).collect()
    }
    
    pub fn doc_count(&self) -> u64 {
        self.doc_ids.len()
    }
    
    pub fn term_frequency(&self, doc_id: DocId) -> u16 {
        // Fast path: lookup in positions map (O(1))
        if let Some(ref pos_map) = self.positions {
            pos_map.get(&doc_id).map(|v| v.len() as u16).unwrap_or(1)
        } else {
            // No positions tracked, assume 1
            if self.doc_ids.contains(doc_id as u32) { 1 } else { 0 }
        }
    }
    
    /// Check if a document exists in the posting list
    pub fn contains(&self, doc_id: DocId) -> bool {
        self.doc_ids.contains(doc_id as u32)
    }
    
    pub fn get_positions(&self, doc_id: DocId) -> Option<&[Position]> {
        self.positions.as_ref()?.get(&doc_id).map(|v| v.as_slice())
    }
    
    /// Remove a document from the posting list
    pub fn remove(&mut self, doc_id: DocId) {
        if !self.doc_ids.contains(doc_id as u32) {
            return;
        }
        
        // Remove from doc_ids bitmap
        self.doc_ids.remove(doc_id as u32);
        
        // Remove from positions if present
        if let Some(ref mut pos_map) = self.positions {
            pos_map.remove(&doc_id);
        }
        
        // Rebuild doc_freqs array to maintain parallel structure
        self.rebuild_doc_freqs_array();
    }
    
    /// Check if posting list is empty
    pub fn is_empty(&self) -> bool {
        self.doc_ids.is_empty()
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    
    #[test]
    fn test_whitespace_tokenizer() {
        let tokenizer = WhitespaceTokenizer::default();
        let tokens = tokenizer.tokenize("Hello, World!");
        assert_eq!(tokens.len(), 2);
        assert_eq!(tokens[0].text, "hello");
        assert_eq!(tokens[1].text, "world");
    }
    
    #[test]
    fn test_ngram_tokenizer() {
        let tokenizer = NgramTokenizer::new(2);
        let tokens = tokenizer.tokenize("rust");
        assert_eq!(tokens.len(), 3);
        assert_eq!(tokens[0].text, "ru");
        assert_eq!(tokens[1].text, "us");
        assert_eq!(tokens[2].text, "st");
    }
    
    #[test]
    fn test_posting_list() {
        let mut posting = PostingList::new();
        posting.add(1, Some(0));
        posting.add(1, Some(5));
        posting.add(2, Some(3));
        
        assert_eq!(posting.doc_count(), 2);
        assert_eq!(posting.term_frequency(1), 2);
        assert_eq!(posting.term_frequency(2), 1);
    }
}