py: reverse order of HnswMap and Hnsw

instant-distance-py/src/lib.rs

@@ -23,63 +23,6 @@ fn instant_distance(_: Python, m: &PyModule) -> PyResult<()> {
     Ok(())
 }
 
-/// An instance of hierarchical navigable small worlds
-///
-/// For now, this is specialized to only support 300-element (32-bit) float vectors
-/// with a squared Euclidean distance metric.
-#[pyclass]
-struct Hnsw {
-    inner: instant_distance::Hnsw<FloatArray>,
-}
-
-#[pymethods]
-impl Hnsw {
-    /// Build the index
-    #[staticmethod]
-    fn build(input: &PyList, config: &Config) -> PyResult<(Self, Vec<u32>)> {
-        let points = input
-            .into_iter()
-            .map(FloatArray::try_from)
-            .collect::<Result<Vec<_>, PyErr>>()?;
-
-        let (inner, ids) = instant_distance::Builder::from(config).build_hnsw(points);
-        let ids = Vec::from_iter(ids.into_iter().map(|pid| pid.into_inner()));
-        Ok((Self { inner }, ids))
-    }
-
-    /// Load an index from the given file name
-    #[staticmethod]
-    fn load(fname: &str) -> PyResult<Self> {
-        let hnsw = bincode::deserialize_from::<_, instant_distance::Hnsw<FloatArray>>(
-            BufReader::with_capacity(32 * 1024 * 1024, File::open(fname)?),
-        )
-        .map_err(|e| PyValueError::new_err(format!("deserialization error: {:?}", e)))?;
-        Ok(Self { inner: hnsw })
-    }
-
-    /// Dump the index to the given file name
-    fn dump(&self, fname: &str) -> PyResult<()> {
-        let f = BufWriter::with_capacity(32 * 1024 * 1024, File::create(fname)?);
-        bincode::serialize_into(f, &self.inner)
-            .map_err(|e| PyValueError::new_err(format!("serialization error: {:?}", e)))?;
-        Ok(())
-    }
-
-    /// Search the index for points neighboring the given point
-    ///
-    /// The `search` object contains buffers used for searching. When the search completes,
-    /// iterate over the `Search` to get the results. The number of results should be equal
-    /// to the `ef_search` parameter set in the index's `config`.
-    ///
-    /// For best performance, reusing `Search` objects is recommended.
-    fn search(&self, point: &PyAny, search: &mut Search) -> PyResult<()> {
-        let point = FloatArray::try_from(point)?;
-        let _ = self.inner.search(&point, &mut search.inner);
-        search.cur = Some(0);
-        Ok(())
-    }
-}
-
 #[pyclass]
 struct HnswMap {
     inner: instant_distance::HnswMap<FloatArray, String>,
@@ -137,6 +80,63 @@ impl HnswMap {
     }
 }
 
+/// An instance of hierarchical navigable small worlds
+///
+/// For now, this is specialized to only support 300-element (32-bit) float vectors
+/// with a squared Euclidean distance metric.
+#[pyclass]
+struct Hnsw {
+    inner: instant_distance::Hnsw<FloatArray>,
+}
+
+#[pymethods]
+impl Hnsw {
+    /// Build the index
+    #[staticmethod]
+    fn build(input: &PyList, config: &Config) -> PyResult<(Self, Vec<u32>)> {
+        let points = input
+            .into_iter()
+            .map(FloatArray::try_from)
+            .collect::<Result<Vec<_>, PyErr>>()?;
+
+        let (inner, ids) = instant_distance::Builder::from(config).build_hnsw(points);
+        let ids = Vec::from_iter(ids.into_iter().map(|pid| pid.into_inner()));
+        Ok((Self { inner }, ids))
+    }
+
+    /// Load an index from the given file name
+    #[staticmethod]
+    fn load(fname: &str) -> PyResult<Self> {
+        let hnsw = bincode::deserialize_from::<_, instant_distance::Hnsw<FloatArray>>(
+            BufReader::with_capacity(32 * 1024 * 1024, File::open(fname)?),
+        )
+        .map_err(|e| PyValueError::new_err(format!("deserialization error: {:?}", e)))?;
+        Ok(Self { inner: hnsw })
+    }
+
+    /// Dump the index to the given file name
+    fn dump(&self, fname: &str) -> PyResult<()> {
+        let f = BufWriter::with_capacity(32 * 1024 * 1024, File::create(fname)?);
+        bincode::serialize_into(f, &self.inner)
+            .map_err(|e| PyValueError::new_err(format!("serialization error: {:?}", e)))?;
+        Ok(())
+    }
+
+    /// Search the index for points neighboring the given point
+    ///
+    /// The `search` object contains buffers used for searching. When the search completes,
+    /// iterate over the `Search` to get the results. The number of results should be equal
+    /// to the `ef_search` parameter set in the index's `config`.
+    ///
+    /// For best performance, reusing `Search` objects is recommended.
+    fn search(&self, point: &PyAny, search: &mut Search) -> PyResult<()> {
+        let point = FloatArray::try_from(point)?;
+        let _ = self.inner.search(&point, &mut search.inner);
+        search.cur = Some(0);
+        Ok(())
+    }
+}
+
 /// Search buffer and result set
 #[pyclass]
 struct Search {