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