feat(bindings): expose extractor for direct usage in Python

examples/python_basic.py

@@ -15,6 +15,9 @@ def main():
     extractor = CharacterNgrams(n=2, endmarker="$")
     print("Using extractor: CharacterNgrams(n=2, endmarker='$')")
 
+    sample_embedding = extractor.apply("Some text")
+    print(f"Sample embedding for 'Some text': {sample_embedding}")
+
     # Choose a similarity measure.
     # Options: Cosine(), Dice(), Jaccard(), Overlap(), ExactMatch()
     measure = Cosine()

src/python/mod.rs

@@ -36,6 +36,16 @@ impl PyCharacterNgrams {
     fn new(n: usize, endmarker: &str) -> Self {
         Self(CharacterNgrams::new(n, endmarker))
     }
+
+    fn apply(&self, text: &str) -> Vec<String> {
+        let mut interner = lasso::Rodeo::default();
+        let features = self.0.features(text, &mut interner);
+
+        features
+            .into_iter()
+            .map(|spur| interner.resolve(&spur).to_string())
+            .collect()
+    }
 }
 
 #[pyclass(name = "WordNgrams")]
@@ -48,6 +58,16 @@ impl PyWordNgrams {
     fn new(n: usize, splitter: &str, padder: &str) -> Self {
         Self(WordNgrams::new(n, splitter, padder))
     }
+
+    fn apply(&self, text: &str) -> Vec<String> {
+        let mut interner = lasso::Rodeo::default();
+        let features = self.0.features(text, &mut interner);
+
+        features
+            .into_iter()
+            .map(|spur| interner.resolve(&spur).to_string())
+            .collect()
+    }
 }
 
 // Wrapper for Measure trait

tests/python/test_bindings.py

@@ -1,7 +1,9 @@
 import pytest
+from collections import Counter
+
 from simstring_rust.database import HashDb
 from simstring_rust.errors import SearchError
-from simstring_rust.extractors import CharacterNgrams
+from simstring_rust.extractors import CharacterNgrams, WordNgrams
 from simstring_rust.measures import Cosine
 from simstring_rust.searcher import Searcher
 
@@ -66,3 +68,17 @@ def test_search_error_on_invalid_threshold(self):
 
         with pytest.raises(SearchError, match=r"Invalid threshold: 0(\.0)?"):
             self.searcher.search("test", 0.0)
+
+    def test_character_ngram_apply(self):
+        extractor = CharacterNgrams(n=2, endmarker="$")
+        features = extractor.apply("apple")
+
+        expected = ["$a1", "ap1", "pp1", "pl1", "le1", "e$1"]
+        assert Counter(features) == Counter(expected)
+
+    def test_word_ngram_apply(self):
+        extractor = WordNgrams(n=2, splitter=" ", padder="#")
+        features = extractor.apply("foo bar baz")
+
+        expected = ["# foo1", "foo bar1", "bar baz1", "baz #1"]
+        assert Counter(features) == Counter(expected)