Expose Tantivy's MoreLikeThisQuery (#257)

src/query.rs

@@ -1,4 +1,4 @@
-use crate::{get_field, make_term, to_pyerr, Schema};
+use crate::{get_field, make_term, to_pyerr, DocAddress, Schema};
 use pyo3::{
     exceptions,
     prelude::*,
@@ -100,7 +100,7 @@ impl Query {
         let terms = field_values
             .into_iter()
             .map(|field_value| {
-                make_term(&schema.inner, field_name, &field_value)
+                make_term(&schema.inner, field_name, field_value)
             })
             .collect::<Result<Vec<_>, _>>()?;
         let inner = tv::query::TermSetQuery::new(terms);
@@ -138,7 +138,7 @@ impl Query {
         transposition_cost_one: bool,
         prefix: bool,
     ) -> PyResult<Query> {
-        let term = make_term(&schema.inner, field_name, &text)?;
+        let term = make_term(&schema.inner, field_name, text)?;
         let inner = if prefix {
             tv::query::FuzzyTermQuery::new_prefix(
                 term,
@@ -272,6 +272,50 @@ impl Query {
         }
     }
 
+    #[staticmethod]
+    #[pyo3(signature = (doc_address, min_doc_frequency = Some(5), max_doc_frequency = None, min_term_frequency = Some(2), max_query_terms = Some(25), min_word_length = None, max_word_length = None, boost_factor = Some(1.0), stop_words = vec![]))]
+    #[allow(clippy::too_many_arguments)]
+    pub(crate) fn more_like_this_query(
+        doc_address: &DocAddress,
+        min_doc_frequency: Option<u64>,
+        max_doc_frequency: Option<u64>,
+        min_term_frequency: Option<usize>,
+        max_query_terms: Option<usize>,
+        min_word_length: Option<usize>,
+        max_word_length: Option<usize>,
+        boost_factor: Option<f32>,
+        stop_words: Vec<String>,
+    ) -> PyResult<Query> {
+        let mut builder = tv::query::MoreLikeThisQuery::builder();
+        if let Some(value) = min_doc_frequency {
+            builder = builder.with_min_doc_frequency(value);
+        }
+        if let Some(value) = max_doc_frequency {
+            builder = builder.with_max_doc_frequency(value);
+        }
+        if let Some(value) = min_term_frequency {
+            builder = builder.with_min_term_frequency(value);
+        }
+        if let Some(value) = max_query_terms {
+            builder = builder.with_max_query_terms(value);
+        }
+        if let Some(value) = min_word_length {
+            builder = builder.with_min_word_length(value);
+        }
+        if let Some(value) = max_word_length {
+            builder = builder.with_max_word_length(value);
+        }
+        if let Some(value) = boost_factor {
+            builder = builder.with_boost_factor(value);
+        }
+        builder = builder.with_stop_words(stop_words);
+
+        let inner = builder.with_document(tv::DocAddress::from(doc_address));
+        Ok(Query {
+            inner: Box::new(inner),
+        })
+    }
+
     /// Construct a Tantivy's ConstScoreQuery
     #[staticmethod]
     #[pyo3(signature = (query, score))]

src/searcher.rs

@@ -153,6 +153,7 @@ impl Searcher {
     ///
     /// Raises a ValueError if there was an error with the search.
     #[pyo3(signature = (query, limit = 10, count = true, order_by_field = None, offset = 0, order = Order::Desc))]
+    #[allow(clippy::too_many_arguments)]
     fn search(
         &self,
         py: Python,

tantivy/tantivy.pyi

@@ -227,6 +227,20 @@ class Query:
     def regex_query(schema: Schema, field_name: str, regex_pattern: str) -> Query:
         pass
 
+    @staticmethod
+    def more_like_this_query(
+        doc_address: DocAddress,
+        min_doc_frequency: Optional[int] = 5,
+        max_doc_frequency: Optional[int] = None,
+        min_term_frequency: Optional[int] = 2,
+        max_query_terms: Optional[int] = 25,
+        min_word_length: Optional[int] = None,
+        max_word_length: Optional[int] = None,
+        boost_factor: Optional[float] = 1.0,
+        stop_words: list[str] = []
+    ) -> Query:
+        pass
+
     @staticmethod
     def const_score_query(query: Query, score: float) -> Query:
         pass

tests/tantivy_test.py

@@ -1090,6 +1090,42 @@ class TestQuery(object):
         ):
             Query.regex_query(index.schema, "body", "fish(")
 
+    def test_more_like_this_query(self, ram_index):
+        index = ram_index
+
+        # first, search the target doc
+        query = Query.term_query(index.schema, "title", "man")
+        result = index.searcher().search(query, 1)
+        _, doc_address = result.hits[0]
+        searched_doc = index.searcher().doc(doc_address)
+        assert searched_doc["title"] == ["The Old Man and the Sea"]
+
+        # construct the default MLT Query
+        mlt_query = Query.more_like_this_query(doc_address)
+        assert (
+            repr(mlt_query)
+            == "Query(MoreLikeThisQuery { mlt: MoreLikeThis { min_doc_frequency: Some(5), max_doc_frequency: None, min_term_frequency: Some(2), max_query_terms: Some(25), min_word_length: None, max_word_length: None, boost_factor: Some(1.0), stop_words: [] }, target: DocumentAdress(DocAddress { segment_ord: 0, doc_id: 0 }) })"
+        )
+        result = index.searcher().search(mlt_query, 10)
+        assert len(result.hits) == 0
+
+        # construct a fine-tuned MLT Query
+        mlt_query = Query.more_like_this_query(
+            doc_address,
+            min_doc_frequency=2,
+            max_doc_frequency=10,
+            min_term_frequency=1,
+            max_query_terms=10,
+            min_word_length=2,
+            max_word_length=20,
+            boost_factor=2.0,
+            stop_words=["fish"])
+        assert (
+            repr(mlt_query)
+            == "Query(MoreLikeThisQuery { mlt: MoreLikeThis { min_doc_frequency: Some(2), max_doc_frequency: Some(10), min_term_frequency: Some(1), max_query_terms: Some(10), min_word_length: Some(2), max_word_length: Some(20), boost_factor: Some(2.0), stop_words: [\"fish\"] }, target: DocumentAdress(DocAddress { segment_ord: 0, doc_id: 0 }) })"
+        )
+        result = index.searcher().search(mlt_query, 10)
+        assert len(result.hits) > 0
     def test_const_score_query(self, ram_index):
         index = ram_index