mirror of
https://gitlab.com/ArkHost/HelixNotes.git
synced 2026-07-23 23:35:57 +02:00
Add CJK (Chinese/Japanese/Korean) tokenizer for full-text search
This commit is contained in:
+218
-21
@@ -9,11 +9,149 @@ use tantivy::collector::TopDocs;
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use tantivy::directory::MmapDirectory;
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#[cfg(mobile)]
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use tantivy::directory::RamDirectory;
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use tantivy::query::{BooleanQuery, FuzzyTermQuery, Occur, PhrasePrefixQuery, Query};
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use tantivy::query::{BooleanQuery, FuzzyTermQuery, Occur, PhrasePrefixQuery, Query, TermQuery};
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use tantivy::schema::*;
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use tantivy::tokenizer::{LowerCaser, TextAnalyzer, Token, TokenStream, Tokenizer};
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use tantivy::{Index, IndexWriter, TantivyDocument, Term};
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use walkdir::WalkDir;
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/// Bumped whenever the index schema or tokenizer changes, so the on-disk index is
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/// wiped and rebuilt once on the next vault open (the index is derived from the
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/// notes, so this never loses data).
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const INDEX_SCHEMA_VERSION: &str = "2-cjk-bigram";
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/// True for characters in the CJK / Japanese / Korean blocks, which are written
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/// without spaces between words. These get uni/bi-gram tokenized so substring
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/// search works; everything else keeps the default word-splitting behaviour.
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fn is_cjk(c: char) -> bool {
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matches!(c as u32,
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0x3400..=0x4DBF // CJK Unified Ideographs Extension A
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| 0x4E00..=0x9FFF // CJK Unified Ideographs (Han)
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| 0x3040..=0x309F // Hiragana
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| 0x30A0..=0x30FF // Katakana
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| 0xAC00..=0xD7AF // Hangul Syllables
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| 0xF900..=0xFAFF // CJK Compatibility Ideographs
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| 0xFF00..=0xFFEF // Halfwidth and Fullwidth Forms
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)
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}
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/// Tokenize `text` so that CJK runs become overlapping unigrams + bigrams (recall
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/// over precision: a query for any substring of a CJK run will match), while runs
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/// of other alphanumeric characters become a single word token (same as tantivy's
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/// SimpleTokenizer, so Latin/English indexing is unchanged). Lowercasing is applied
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/// by a LowerCaser filter in the analyzer, not here.
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fn cjk_tokens(text: &str) -> Vec<Token> {
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let mut tokens: Vec<Token> = Vec::new();
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let mut position: usize = 0;
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let mut cjk_run: Vec<(usize, char)> = Vec::new();
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let mut word = String::new();
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let mut word_start: usize = 0;
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macro_rules! flush_word {
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() => {{
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if !word.is_empty() {
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let len = word.len();
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tokens.push(Token {
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offset_from: word_start,
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offset_to: word_start + len,
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position,
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text: std::mem::take(&mut word),
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position_length: 1,
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});
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position += 1;
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}
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}};
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}
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macro_rules! flush_cjk {
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() => {{
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let n = cjk_run.len();
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for i in 0..n {
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let (off, ch) = cjk_run[i];
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tokens.push(Token {
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offset_from: off,
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offset_to: off + ch.len_utf8(),
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position,
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text: ch.to_string(),
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position_length: 1,
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});
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position += 1;
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}
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for i in 0..n.saturating_sub(1) {
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let (off1, c1) = cjk_run[i];
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let (off2, c2) = cjk_run[i + 1];
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let mut s = String::with_capacity(c1.len_utf8() + c2.len_utf8());
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s.push(c1);
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s.push(c2);
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tokens.push(Token {
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offset_from: off1,
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offset_to: off2 + c2.len_utf8(),
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position,
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text: s,
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position_length: 1,
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});
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position += 1;
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}
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cjk_run.clear();
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}};
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}
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for (offset, c) in text.char_indices() {
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if is_cjk(c) {
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flush_word!();
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cjk_run.push((offset, c));
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} else if c.is_alphanumeric() {
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flush_cjk!();
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if word.is_empty() {
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word_start = offset;
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}
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word.push(c);
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} else {
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flush_cjk!();
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flush_word!();
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}
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}
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flush_cjk!();
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flush_word!();
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let _ = position; // the final flush increments position but nothing reads it after
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tokens
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}
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/// A pre-computed token stream (all tokens collected up front by `cjk_tokens`).
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struct PreTokenizedStream {
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tokens: Vec<Token>,
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idx: usize,
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}
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impl TokenStream for PreTokenizedStream {
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fn advance(&mut self) -> bool {
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if self.idx < self.tokens.len() {
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self.idx += 1;
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true
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} else {
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false
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}
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}
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fn token(&self) -> &Token {
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&self.tokens[self.idx - 1]
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}
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fn token_mut(&mut self) -> &mut Token {
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&mut self.tokens[self.idx - 1]
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}
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}
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#[derive(Clone)]
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struct CjkTokenizer;
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impl Tokenizer for CjkTokenizer {
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type TokenStream<'a> = PreTokenizedStream;
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fn token_stream<'a>(&'a mut self, text: &'a str) -> PreTokenizedStream {
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PreTokenizedStream {
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tokens: cjk_tokens(text),
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idx: 0,
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}
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}
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}
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pub struct SearchIndex {
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index: Index,
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writer: Mutex<Option<IndexWriter>>,
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@@ -29,9 +167,16 @@ impl SearchIndex {
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pub fn new(vault_path: &str) -> Result<Self, String> {
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let mut schema_builder = Schema::builder();
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let path_field = schema_builder.add_text_field("path", STRING | STORED);
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let title_field = schema_builder.add_text_field("title", TEXT | STORED);
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let body_field = schema_builder.add_text_field("body", TEXT);
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let tags_field = schema_builder.add_text_field("tags", TEXT | STORED);
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// CJK-aware tokenizer for the indexed text fields (keeps Latin/English
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// behaviour identical; adds substring matching for Chinese/Japanese/Korean).
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let cjk_indexing = TextFieldIndexing::default()
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.set_tokenizer("cjk")
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.set_index_option(IndexRecordOption::WithFreqsAndPositions);
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let cjk_text = TextOptions::default().set_indexing_options(cjk_indexing.clone());
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let cjk_text_stored = cjk_text.clone().set_stored();
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let title_field = schema_builder.add_text_field("title", cjk_text_stored.clone());
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let body_field = schema_builder.add_text_field("body", cjk_text);
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let tags_field = schema_builder.add_text_field("tags", cjk_text_stored);
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let schema = schema_builder.build();
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// Mobile: use in-memory index (flock is unreliable on the sandboxed/FUSE filesystem)
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@@ -43,12 +188,42 @@ impl SearchIndex {
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};
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#[cfg(desktop)]
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let index = {
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let index_dir = helixnotes_dir(vault_path).join("search_index");
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let hn = helixnotes_dir(vault_path);
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let index_dir = hn.join("search_index");
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let version_path = hn.join("search_index.version");
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// One-time wipe when the schema/tokenizer version changes, so a stale
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// index built with the old tokenizer is rebuilt. open_vault always calls
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// rebuild() afterwards, and the index is derived from the .md files, so
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// wiping never loses data.
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let version_ok = fs::read_to_string(&version_path)
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.map(|v| v.trim() == INDEX_SCHEMA_VERSION)
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.unwrap_or(false);
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if !version_ok {
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let _ = fs::remove_dir_all(&index_dir);
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}
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fs::create_dir_all(&index_dir).map_err(|e| e.to_string())?;
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let dir = MmapDirectory::open(&index_dir).map_err(|e| e.to_string())?;
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Index::open_or_create(dir, schema.clone()).map_err(|e| e.to_string())?
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let idx = match Index::open_or_create(dir, schema.clone()) {
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Ok(idx) => idx,
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Err(_) => {
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// Schema mismatch (older index) or corruption: wipe and recreate.
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let _ = fs::remove_dir_all(&index_dir);
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fs::create_dir_all(&index_dir).map_err(|e| e.to_string())?;
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let dir = MmapDirectory::open(&index_dir).map_err(|e| e.to_string())?;
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Index::open_or_create(dir, schema.clone()).map_err(|e| e.to_string())?
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}
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};
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let _ = fs::write(&version_path, INDEX_SCHEMA_VERSION);
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idx
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};
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// Register the CJK-aware tokenizer (in-memory; must be done on every open,
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// before the writer is created, so both indexing and querying use it).
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index.tokenizers().register(
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"cjk",
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TextAnalyzer::builder(CjkTokenizer).filter(LowerCaser).build(),
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);
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#[cfg(mobile)]
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let heap_size = 15_000_000;
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#[cfg(desktop)]
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@@ -153,28 +328,50 @@ impl SearchIndex {
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let searcher = reader.searcher();
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let fields = vec![self.title_field, self.body_field, self.tags_field];
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let terms: Vec<String> = query_str
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.split_whitespace()
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.map(|t| t.to_lowercase())
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.collect();
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// Tokenize the query with the SAME CJK-aware analyzer used for indexing, so a
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// Chinese/Japanese/Korean query becomes the same uni/bigram tokens as the docs.
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// (For pure-ASCII queries this yields the same lowercased word tokens as before.)
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let mut analyzer = self
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.index
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.tokenizers()
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.get("cjk")
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.ok_or("cjk tokenizer not registered")?;
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let terms: Vec<String> = {
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let mut out = Vec::new();
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let mut stream = analyzer.token_stream(query_str);
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while stream.advance() {
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out.push(stream.token().text.clone());
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}
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out
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};
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// For each search term, create prefix + fuzzy queries across all fields (OR),
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// then AND all terms together
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// For each term, OR queries across all fields, then AND all terms together.
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// CJK tokens (uni/bigrams) already encode segmentation -> exact term match.
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// Latin tokens keep prefix + fuzzy matching (unchanged English behaviour).
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let term_queries: Vec<(Occur, Box<dyn Query>)> = terms
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.iter()
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.map(|term| {
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let is_cjk_term = term.chars().any(is_cjk);
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let field_queries: Vec<(Occur, Box<dyn Query>)> = fields
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.iter()
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.flat_map(|&field| {
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let prefix: Box<dyn Query> = Box::new(PhrasePrefixQuery::new(
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vec![Term::from_field_text(field, term)],
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));
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let fuzzy: Box<dyn Query> = Box::new(FuzzyTermQuery::new(
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Term::from_field_text(field, term),
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1,
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true,
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));
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vec![(Occur::Should, prefix), (Occur::Should, fuzzy)]
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if is_cjk_term {
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let exact: Box<dyn Query> = Box::new(TermQuery::new(
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Term::from_field_text(field, term),
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IndexRecordOption::WithFreqs,
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));
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vec![(Occur::Should, exact)]
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} else {
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let prefix: Box<dyn Query> = Box::new(PhrasePrefixQuery::new(
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vec![Term::from_field_text(field, term)],
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));
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let fuzzy: Box<dyn Query> = Box::new(FuzzyTermQuery::new(
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Term::from_field_text(field, term),
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1,
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true,
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));
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vec![(Occur::Should, prefix), (Occur::Should, fuzzy)]
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}
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})
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.collect();
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let combined: Box<dyn Query> = Box::new(BooleanQuery::new(field_queries));
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