feat: save safetensors to the database

Cargo.lock

@@ -269,6 +269,20 @@ name = "bytemuck"
 version = "1.23.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "3995eaeebcdf32f91f980d360f78732ddc061097ab4e39991ae7a6ace9194677"
+dependencies = [
+ "bytemuck_derive",
+]
+
+[[package]]
+name = "bytemuck_derive"
+version = "1.10.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "4f154e572231cb6ba2bd1176980827e3d5dc04cc183a75dea38109fbdd672d29"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn",
+]
 
 [[package]]
 name = "byteorder-lite"
@@ -504,7 +518,9 @@ dependencies = [
  "nalgebra",
  "ndarray",
  "ndarray-image",
+ "ndarray-math",
  "ndarray-resize",
+ "ndarray-safetensors",
  "ordered-float",
  "ort",
  "rusqlite",
@@ -830,6 +846,7 @@ version = "2.6.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "459196ed295495a68f7d7fe1d84f6c4b7ff0e21fe3017b2f283c6fac3ad803c9"
 dependencies = [
+ "bytemuck",
  "cfg-if",
  "crunchy",
 ]
@@ -1414,6 +1431,16 @@ dependencies = [
  "ndarray",
 ]
 
+[[package]]
+name = "ndarray-math"
+version = "0.1.0"
+source = "git+https://git.darksailor.dev/servius/ndarray-math#f047966f20835267f20e5839272b9ab36c445796"
+dependencies = [
+ "ndarray",
+ "num",
+ "thiserror 2.0.15",
+]
+
 [[package]]
 name = "ndarray-resize"
 version = "0.1.0"
@@ -1426,6 +1453,17 @@ dependencies = [
  "thiserror 2.0.15",
 ]
 
+[[package]]
+name = "ndarray-safetensors"
+version = "0.1.0"
+dependencies = [
+ "bytemuck",
+ "half",
+ "ndarray",
+ "safetensors",
+ "thiserror 2.0.15",
+]
+
 [[package]]
 name = "new_debug_unreachable"
 version = "1.0.6"
@@ -1983,6 +2021,16 @@ dependencies = [
  "bytemuck",
 ]
 
+[[package]]
+name = "safetensors"
+version = "0.6.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "172dd94c5a87b5c79f945c863da53b2ebc7ccef4eca24ac63cca66a41aab2178"
+dependencies = [
+ "serde",
+ "serde_json",
+]
+
 [[package]]
 name = "scopeguard"
 version = "1.2.0"

Cargo.toml

@@ -1,5 +1,5 @@
 [workspace]
-members = ["ndarray-image", "ndarray-resize", ".", "bounding-box"]
+members = ["ndarray-image", "ndarray-resize", ".", "bounding-box", "ndarray-safetensors"]
 
 [workspace.package]
 version = "0.1.0"
@@ -37,7 +37,7 @@ nalgebra = { workspace = true }
 ndarray = "0.16.1"
 ndarray-image = { workspace = true }
 ndarray-resize = { workspace = true }
-rusqlite = { version = "0.37.0", features = ["modern-full"] }
+rusqlite = { version = "0.37.0", features = ["functions", "modern-full"] }
 tap = "1.0.1"
 thiserror = "2.0"
 tokio = "1.43.1"
@@ -50,11 +50,9 @@ bounding-box = { version = "0.1.0", path = "bounding-box" }
 color = "0.3.1"
 itertools = "0.14.0"
 ordered-float = "5.0.0"
-ort = { version = "2.0.0-rc.10", default-features = false, features = [
-    "std",
-    "tracing",
-    "ndarray",
-] }
+ort = { version = "2.0.0-rc.10", default-features = false, features = [ "std", "tracing", "ndarray"]}
+ndarray-math = { git = "https://git.darksailor.dev/servius/ndarray-math", version = "0.1.0" }
+ndarray-safetensors = { version = "0.1.0", path = "ndarray-safetensors" }
 
 [profile.release]
 debug = true

flake.nix

@@ -114,16 +114,17 @@
             stdenv = p: p.clangStdenv;
             doCheck = false;
             LIBCLANG_PATH = "${pkgs.llvmPackages.libclang.lib}/lib";
-            ORT_LIB_LOCATION = "${patchedOnnxruntime}";
-            ORT_ENV_SYSTEM_LIB_LOCATION = "${patchedOnnxruntime}/lib";
-            ORT_ENV_PREFER_DYNAMIC_LINK = true;
+            # ORT_LIB_LOCATION = "${patchedOnnxruntime}";
+            # ORT_ENV_SYSTEM_LIB_LOCATION = "${patchedOnnxruntime}/lib";
+            # ORT_ENV_PREFER_DYNAMIC_LINK = true;
             nativeBuildInputs = with pkgs; [
               cmake
               pkg-config
             ];
             buildInputs = with pkgs;
               [
-                # onnxruntime
+                patchedOnnxruntime
+                sqlite
               ]
               ++ (lib.optionals pkgs.stdenv.isDarwin [
                 libiconv

justfile

@@ -9,5 +9,5 @@ open:
 bench:
     cargo build --release
     BINARY="" hyperfine --warmup 3 --export-markdown benchmark.md \
-        "$CARGO_TARGET_DIR/release/detector detect -f coreml selfie.jpg" \
-        "$CARGO_TARGET_DIR/release/detector detect -f coreml -b 16 selfie.jpg"
+        "$CARGO_TARGET_DIR/release/detector detect -f cpu selfie.jpg" \
+        "$CARGO_TARGET_DIR/release/detector detect -f cpu -b 1 selfie.jpg"

ndarray-safetensors/Cargo.toml

@@ -0,0 +1,11 @@
+[package]
+name = "ndarray-safetensors"
+version.workspace = true
+edition.workspace = true
+
+[dependencies]
+bytemuck = { version = "1.23.2" }
+half = { version = "2.6.0", default-features = false, features = ["bytemuck"] }
+ndarray = { version = "0.16.1", default-features = false, features = ["std"] }
+safetensors = "0.6.2"
+thiserror = "2.0.15"

ndarray-safetensors/src/lib.rs

@@ -0,0 +1,432 @@
+//! # ndarray-serialize
+//!
+//! A Rust library for serializing and deserializing `ndarray` arrays using the SafeTensors format.
+//!
+//! ## Features
+//! - Serialize `ndarray::ArrayView` to SafeTensors format
+//! - Deserialize SafeTensors data back to `ndarray::ArrayView`
+//! - Support for multiple data types (f32, f64, i8-i64, u8-u64, f16, bf16)
+//! - Zero-copy deserialization when possible
+//! - Metadata support
+//!
+//! ## Example
+//! ```rust
+//! use ndarray::Array2;
+//! use ndarray_safetensors::{SafeArrays, SafeArrayView};
+//!
+//! // Create some data
+//! let array = Array2::<f32>::zeros((3, 4));
+//!
+//! // Serialize
+//! let mut safe_arrays = SafeArrays::new();
+//! safe_arrays.insert_ndarray("my_tensor", array.view()).unwrap();
+//! safe_arrays.insert_metadata("author", "example");
+//! let bytes = safe_arrays.serialize().unwrap();
+//!
+//! // Deserialize
+//! let view = SafeArrayView::from_bytes(&bytes).unwrap();
+//! let tensor: ndarray::ArrayView2<f32> = view.tensor("my_tensor").unwrap();
+//! assert_eq!(tensor.shape(), &[3, 4]);
+//! ```
+
+use safetensors::View;
+use std::borrow::Cow;
+use std::collections::{BTreeMap, HashMap};
+
+use thiserror::Error;
+/// Errors that can occur during SafeTensor operations
+#[derive(Error, Debug)]
+pub enum SafeTensorError {
+    #[error("Tensor not found: {0}")]
+    TensorNotFound(String),
+    #[error("Invalid tensor data: Got {0} Expected: {1}")]
+    InvalidTensorData(&'static str, String),
+    #[error("IO error: {0}")]
+    IoError(#[from] std::io::Error),
+    #[error("Safetensor error: {0}")]
+    SafeTensor(#[from] safetensors::SafeTensorError),
+    #[error("ndarray::ShapeError error: {0}")]
+    NdarrayShapeError(#[from] ndarray::ShapeError),
+}
+
+type Result<T, E = SafeTensorError> = core::result::Result<T, E>;
+
+use safetensors::tensor::SafeTensors;
+
+/// A view into SafeTensors data that provides access to ndarray tensors
+///
+/// # Example
+/// ```rust
+/// use ndarray::Array2;
+/// use ndarray_safetensors::{SafeArrays, SafeArrayView};
+///
+/// let array = Array2::<f32>::ones((2, 3));
+/// let mut safe_arrays = SafeArrays::new();
+/// safe_arrays.insert_ndarray("data", array.view()).unwrap();
+/// let bytes = safe_arrays.serialize().unwrap();
+///
+/// let view = SafeArrayView::from_bytes(&bytes).unwrap();
+/// let tensor: ndarray::ArrayView2<f32> = view.tensor("data").unwrap();
+/// ```
+pub struct SafeArraysView<'a> {
+    pub tensors: SafeTensors<'a>,
+}
+
+impl<'a> SafeArraysView<'a> {
+    fn new(tensors: SafeTensors<'a>) -> Self {
+        Self { tensors }
+    }
+
+    /// Create a SafeArrayView from serialized bytes
+    pub fn from_bytes(bytes: &'a [u8]) -> Result<SafeArraysView<'a>> {
+        let tensors = SafeTensors::deserialize(bytes)?;
+        Ok(Self::new(tensors))
+    }
+
+    /// Get a dynamic-dimensional tensor by name
+    pub fn dynamic_tensor<T: STDtype>(&self, name: &str) -> Result<ndarray::ArrayViewD<'a, T>> {
+        self.tensors
+            .tensor(name)
+            .map(|tensor| tensor_view_to_array_view(tensor))?
+    }
+
+    /// Get a tensor with specific dimensions by name
+    ///
+    /// # Example
+    /// ```rust
+    /// # use ndarray::Array2;
+    /// # use ndarray_safetensors::{SafeArrays, SafeArrayView};
+    /// # let array = Array2::<f32>::ones((2, 3));
+    /// # let mut safe_arrays = SafeArrays::new();
+    /// # safe_arrays.insert_ndarray("data", array.view()).unwrap();
+    /// # let bytes = safe_arrays.serialize().unwrap();
+    /// # let view = SafeArrayView::from_bytes(&bytes).unwrap();
+    /// let tensor: ndarray::ArrayView2<f32> = view.tensor("data").unwrap();
+    /// ```
+    pub fn tensor<T: STDtype, Dim: ndarray::Dimension>(
+        &self,
+        name: &str,
+    ) -> Result<ndarray::ArrayView<'a, T, Dim>> {
+        Ok(self
+            .tensors
+            .tensor(name)
+            .map(|tensor| tensor_view_to_array_view(tensor))?
+            .map(|array_view| array_view.into_dimensionality::<Dim>())??)
+    }
+
+    /// Get an iterator over tensor names
+    pub fn names(&self) -> std::vec::IntoIter<&str> {
+        self.tensors.names().into_iter()
+    }
+
+    /// Get the number of tensors
+    pub fn len(&self) -> usize {
+        self.tensors.len()
+    }
+
+    /// Check if there are no tensors
+    pub fn is_empty(&self) -> bool {
+        self.tensors.is_empty()
+    }
+}
+
+/// Trait for types that can be stored in SafeTensors
+///
+/// Implemented for: f32, f64, i8, i16, i32, i64, u8, u16, u32, u64, f16, bf16
+pub trait STDtype: bytemuck::Pod {
+    fn dtype() -> safetensors::tensor::Dtype;
+    fn size() -> usize {
+        (Self::dtype().bitsize() / 8).max(1)
+    }
+}
+
+macro_rules! impl_dtype {
+    ($($t:ty => $dtype:expr),* $(,)?) => {
+        $(
+            impl STDtype for $t {
+                fn dtype() -> safetensors::tensor::Dtype {
+                    $dtype
+                }
+            }
+        )*
+    };
+}
+
+use safetensors::tensor::Dtype;
+
+impl_dtype!(
+    // bool => Dtype::BOOL, // idk if ndarray::ArrayD<bool> is packed
+    f32 => Dtype::F32,
+    f64 => Dtype::F64,
+    i8 => Dtype::I8,
+    i16 => Dtype::I16,
+    i32 => Dtype::I32,
+    i64 => Dtype::I64,
+    u8 => Dtype::U8,
+    u16 => Dtype::U16,
+    u32 => Dtype::U32,
+    u64 => Dtype::U64,
+    half::f16 => Dtype::F16,
+    half::bf16 => Dtype::BF16,
+);
+
+fn tensor_view_to_array_view<'a, T: STDtype>(
+    tensor: safetensors::tensor::TensorView<'a>,
+) -> Result<ndarray::ArrayViewD<'a, T>> {
+    let shape = tensor.shape();
+    let dtype = tensor.dtype();
+    if T::dtype() != dtype {
+        return Err(SafeTensorError::InvalidTensorData(
+            core::any::type_name::<T>(),
+            dtype.to_string(),
+        ));
+    }
+
+    let data = tensor.data();
+    let data: &[T] = bytemuck::cast_slice(data);
+    let array = ndarray::ArrayViewD::from_shape(shape, data)?;
+    Ok(array)
+}
+
+/// Builder for creating SafeTensors data from ndarray tensors
+///
+/// # Example
+/// ```rust
+/// use ndarray::{Array1, Array2};
+/// use ndarray_safetensors::SafeArrays;
+///
+/// let mut safe_arrays = SafeArrays::new();
+///
+/// let array1 = Array1::<f32>::from_vec(vec![1.0, 2.0, 3.0]);
+/// let array2 = Array2::<i32>::zeros((2, 2));
+///
+/// safe_arrays.insert_ndarray("vector", array1.view()).unwrap();
+/// safe_arrays.insert_ndarray("matrix", array2.view()).unwrap();
+/// safe_arrays.insert_metadata("version", "1.0");
+///
+/// let bytes = safe_arrays.serialize().unwrap();
+/// ```
+#[derive(Debug, Clone, Default)]
+#[non_exhaustive]
+pub struct SafeArrays<'a> {
+    pub tensors: BTreeMap<String, SafeArray<'a>>,
+    pub metadata: Option<HashMap<String, String>>,
+}
+
+impl<'a, K: AsRef<str>> FromIterator<(K, SafeArray<'a>)> for SafeArrays<'a> {
+    fn from_iter<T: IntoIterator<Item = (K, SafeArray<'a>)>>(iter: T) -> Self {
+        let tensors = iter
+            .into_iter()
+            .map(|(k, v)| (k.as_ref().to_owned(), v))
+            .collect();
+        Self {
+            tensors,
+            metadata: None,
+        }
+    }
+}
+
+impl<'a, K: AsRef<str>, T: IntoIterator<Item = (K, SafeArray<'a>)>> From<T> for SafeArrays<'a> {
+    fn from(iter: T) -> Self {
+        let tensors = iter
+            .into_iter()
+            .map(|(k, v)| (k.as_ref().to_owned(), v))
+            .collect();
+        Self {
+            tensors,
+            metadata: None,
+        }
+    }
+}
+
+impl<'a> SafeArrays<'a> {
+    /// Create a SafeArrays from an iterator of (name, ndarray::ArrayView) pairs
+    /// ```rust
+    /// use ndarray::{Array2, Array3};
+    /// use ndarray_safetensors::{SafeArrays, SafeArray};
+    /// let array = Array2::<f32>::zeros((3, 4));
+    /// let safe_arrays = SafeArrays::from_ndarrays(vec![
+    ///     ("test_tensor", array.view()),
+    ///     ("test_tensor2", array.view()),
+    /// ]).unwrap();
+    /// ```
+
+    pub fn from_ndarrays<
+        K: AsRef<str>,
+        T: STDtype,
+        D: ndarray::Dimension + 'a,
+        I: IntoIterator<Item = (K, ndarray::ArrayView<'a, T, D>)>,
+    >(
+        iter: I,
+    ) -> Result<Self> {
+        let tensors = iter
+            .into_iter()
+            .map(|(k, v)| Ok((k.as_ref().to_owned(), SafeArray::from_ndarray(v)?)))
+            .collect::<Result<BTreeMap<String, SafeArray<'a>>>>()?;
+        Ok(Self {
+            tensors,
+            metadata: None,
+        })
+    }
+}
+
+// impl<'a, K: AsRef<str>, T: IntoIterator<Item = (K, SafeArray<'a>)>> From<T> for SafeArrays<'a> {
+//     fn from(iter: T) -> Self {
+//         let tensors = iter
+//             .into_iter()
+//             .map(|(k, v)| (k.as_ref().to_owned(), v))
+//             .collect();
+//         Self {
+//             tensors,
+//             metadata: None,
+//         }
+//     }
+// }
+
+impl<'a> SafeArrays<'a> {
+    /// Create a new empty SafeArrays builder
+    pub const fn new() -> Self {
+        Self {
+            tensors: BTreeMap::new(),
+            metadata: None,
+        }
+    }
+
+    /// Insert a SafeArray tensor with the given name
+    pub fn insert_tensor<'b: 'a>(&mut self, name: impl AsRef<str>, tensor: SafeArray<'b>) {
+        self.tensors.insert(name.as_ref().to_owned(), tensor);
+    }
+
+    /// Insert an ndarray tensor with the given name
+    ///
+    /// The array must be in standard layout and contiguous.
+    pub fn insert_ndarray<'b: 'a, T: STDtype, D: ndarray::Dimension + 'a>(
+        &mut self,
+        name: impl AsRef<str>,
+        array: ndarray::ArrayView<'b, T, D>,
+    ) -> Result<()> {
+        self.insert_tensor(name, SafeArray::from_ndarray(array)?);
+        Ok(())
+    }
+
+    /// Insert metadata key-value pair
+    pub fn insert_metadata(&mut self, key: impl AsRef<str>, value: impl AsRef<str>) {
+        self.metadata
+            .get_or_insert_default()
+            .insert(key.as_ref().to_owned(), value.as_ref().to_owned());
+    }
+
+    /// Serialize all tensors and metadata to bytes
+    pub fn serialize(self) -> Result<Vec<u8>> {
+        let out = safetensors::serialize(self.tensors, self.metadata)
+            .map_err(SafeTensorError::SafeTensor)?;
+        Ok(out)
+    }
+}
+
+/// A tensor that can be serialized to SafeTensors format
+#[derive(Debug, Clone)]
+pub struct SafeArray<'a> {
+    data: Cow<'a, [u8]>,
+    shape: Vec<usize>,
+    dtype: safetensors::tensor::Dtype,
+}
+
+impl View for SafeArray<'_> {
+    fn dtype(&self) -> safetensors::tensor::Dtype {
+        self.dtype
+    }
+
+    fn shape(&self) -> &[usize] {
+        &self.shape
+    }
+
+    fn data(&self) -> Cow<'_, [u8]> {
+        self.data.clone()
+    }
+
+    fn data_len(&self) -> usize {
+        self.data.len()
+    }
+}
+
+impl<'a> SafeArray<'a> {
+    fn from_ndarray<'b: 'a, T: STDtype, D: ndarray::Dimension + 'a>(
+        array: ndarray::ArrayView<'b, T, D>,
+    ) -> Result<Self> {
+        let shape = array.shape().to_vec();
+        let dtype = T::dtype();
+        if array.ndim() == 0 {
+            return Err(SafeTensorError::InvalidTensorData(
+                core::any::type_name::<T>(),
+                "Cannot insert a scalar tensor".to_string(),
+            ));
+        }
+
+        if !array.is_standard_layout() {
+            return Err(SafeTensorError::InvalidTensorData(
+                core::any::type_name::<T>(),
+                "ArrayView is not standard layout".to_string(),
+            ));
+        }
+        let data =
+            bytemuck::cast_slice(array.to_slice().ok_or(SafeTensorError::InvalidTensorData(
+                core::any::type_name::<T>(),
+                "ArrayView is not contiguous".to_string(),
+            ))?);
+        let safe_array = SafeArray {
+            data: Cow::Borrowed(data),
+            shape,
+            dtype,
+        };
+        Ok(safe_array)
+    }
+}
+
+#[test]
+fn test_safe_array_from_ndarray() {
+    use ndarray::Array2;
+
+    let array = Array2::<f32>::zeros((3, 4));
+    let safe_array = SafeArray::from_ndarray(array.view()).unwrap();
+    assert_eq!(safe_array.shape, vec![3, 4]);
+    assert_eq!(safe_array.dtype, safetensors::tensor::Dtype::F32);
+    assert_eq!(safe_array.data.len(), 3 * 4 * 4); // 3x4x4 bytes for f32
+}
+
+#[test]
+fn test_serialize_safe_arrays() {
+    use ndarray::{Array2, Array3};
+
+    let mut safe_arrays = SafeArrays::new();
+    let array = Array2::<f32>::zeros((3, 4));
+    let array2 = Array3::<u16>::zeros((8, 1, 9));
+    safe_arrays
+        .insert_ndarray("test_tensor", array.view())
+        .unwrap();
+    safe_arrays
+        .insert_ndarray("test_tensor2", array2.view())
+        .unwrap();
+    safe_arrays.insert_metadata("author", "example");
+
+    let serialized = safe_arrays.serialize().unwrap();
+    assert!(!serialized.is_empty());
+
+    // Deserialize to check if it works
+    let deserialized = SafeArraysView::from_bytes(&serialized).unwrap();
+    assert_eq!(deserialized.len(), 2);
+    assert_eq!(
+        deserialized
+            .tensor::<f32, ndarray::Ix2>("test_tensor")
+            .unwrap()
+            .shape(),
+        &[3, 4]
+    );
+    assert_eq!(
+        deserialized
+            .tensor::<u16, ndarray::Ix3>("test_tensor2")
+            .unwrap()
+            .shape(),
+        &[8, 1, 9]
+    );
+}

src/cli.rs

@@ -13,6 +13,12 @@ pub enum SubCommand {
     Detect(Detect),
     #[clap(name = "list")]
     List(List),
+    #[clap(name = "query")]
+    Query(Query),
+    #[clap(name = "similar")]
+    Similar(Similar),
+    #[clap(name = "stats")]
+    Stats(Stats),
     #[clap(name = "completions")]
     Completions { shell: clap_complete::Shell },
 }
@@ -58,12 +64,50 @@ pub struct Detect {
     pub nms_threshold: f32,
     #[clap(short, long, default_value_t = 8)]
     pub batch_size: usize,
+    #[clap(short = 'd', long)]
+    pub database: Option<PathBuf>,
+    #[clap(long, default_value = "facenet")]
+    pub model_name: String,
+    #[clap(long)]
+    pub save_to_db: bool,
     pub image: PathBuf,
 }
 
 #[derive(Debug, clap::Args)]
 pub struct List {}
 
+#[derive(Debug, clap::Args)]
+pub struct Query {
+    #[clap(short = 'd', long, default_value = "face_detections.db")]
+    pub database: PathBuf,
+    #[clap(short, long)]
+    pub image_id: Option<i64>,
+    #[clap(short, long)]
+    pub face_id: Option<i64>,
+    #[clap(long)]
+    pub show_embeddings: bool,
+    #[clap(long)]
+    pub show_landmarks: bool,
+}
+
+#[derive(Debug, clap::Args)]
+pub struct Similar {
+    #[clap(short = 'd', long, default_value = "face_detections.db")]
+    pub database: PathBuf,
+    #[clap(short, long)]
+    pub face_id: i64,
+    #[clap(short, long, default_value_t = 0.7)]
+    pub threshold: f32,
+    #[clap(short, long, default_value_t = 10)]
+    pub limit: usize,
+}
+
+#[derive(Debug, clap::Args)]
+pub struct Stats {
+    #[clap(short = 'd', long, default_value = "face_detections.db")]
+    pub database: PathBuf,
+}
+
 impl Cli {
     pub fn completions(shell: clap_complete::Shell) {
         let mut command = <Cli as clap::CommandFactory>::command();

src/database.rs

@@ -0,0 +1,597 @@
+use crate::errors::{Error, Result};
+use crate::facedet::{FaceDetectionOutput, FaceLandmarks};
+use bounding_box::Aabb2;
+use error_stack::ResultExt;
+use ndarray_math::CosineSimilarity;
+use rusqlite::{Connection, OptionalExtension, params};
+use std::path::Path;
+
+/// Database connection and operations for face detection results
+pub struct FaceDatabase {
+    conn: Connection,
+}
+
+/// Represents a stored image record
+#[derive(Debug, Clone)]
+pub struct ImageRecord {
+    pub id: i64,
+    pub file_path: String,
+    pub width: u32,
+    pub height: u32,
+    pub created_at: String,
+}
+
+/// Represents a stored face detection record
+#[derive(Debug, Clone)]
+pub struct FaceRecord {
+    pub id: i64,
+    pub image_id: i64,
+    pub bbox_x1: f32,
+    pub bbox_y1: f32,
+    pub bbox_x2: f32,
+    pub bbox_y2: f32,
+    pub confidence: f32,
+    pub created_at: String,
+}
+
+/// Represents stored face landmarks
+#[derive(Debug, Clone)]
+pub struct LandmarkRecord {
+    pub id: i64,
+    pub face_id: i64,
+    pub left_eye_x: f32,
+    pub left_eye_y: f32,
+    pub right_eye_x: f32,
+    pub right_eye_y: f32,
+    pub nose_x: f32,
+    pub nose_y: f32,
+    pub left_mouth_x: f32,
+    pub left_mouth_y: f32,
+    pub right_mouth_x: f32,
+    pub right_mouth_y: f32,
+}
+
+/// Represents a stored face embedding
+#[derive(Debug, Clone)]
+pub struct EmbeddingRecord {
+    pub id: i64,
+    pub face_id: i64,
+    pub embedding: ndarray::Array1<f32>,
+    pub model_name: String,
+    pub created_at: String,
+}
+
+impl FaceDatabase {
+    /// Create a new database connection and initialize tables
+    pub fn new<P: AsRef<Path>>(db_path: P) -> Result<Self> {
+        let conn = Connection::open(db_path).change_context(Error)?;
+        add_sqlite_cosine_similarity(&conn).change_context(Error)?;
+        let db = Self { conn };
+        db.create_tables()?;
+        Ok(db)
+    }
+
+    /// Create an in-memory database for testing
+    pub fn in_memory() -> Result<Self> {
+        let conn = Connection::open_in_memory().change_context(Error)?;
+        let db = Self { conn };
+        db.create_tables()?;
+        Ok(db)
+    }
+
+    /// Create all necessary database tables
+    fn create_tables(&self) -> Result<()> {
+        // Images table
+        self.conn
+            .execute(
+                r#"
+                CREATE TABLE IF NOT EXISTS images (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    file_path TEXT NOT NULL UNIQUE,
+                    width INTEGER NOT NULL,
+                    height INTEGER NOT NULL,
+                    created_at DATETIME DEFAULT CURRENT_TIMESTAMP
+                )
+                "#,
+                [],
+            )
+            .change_context(Error)?;
+
+        // Faces table
+        self.conn
+            .execute(
+                r#"
+                CREATE TABLE IF NOT EXISTS faces (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    image_id INTEGER NOT NULL,
+                    bbox_x1 REAL NOT NULL,
+                    bbox_y1 REAL NOT NULL,
+                    bbox_x2 REAL NOT NULL,
+                    bbox_y2 REAL NOT NULL,
+                    confidence REAL NOT NULL,
+                    created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
+                    FOREIGN KEY (image_id) REFERENCES images (id) ON DELETE CASCADE
+                )
+                "#,
+                [],
+            )
+            .change_context(Error)?;
+
+        // Landmarks table
+        self.conn
+            .execute(
+                r#"
+                CREATE TABLE IF NOT EXISTS landmarks (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    face_id INTEGER NOT NULL,
+                    left_eye_x REAL NOT NULL,
+                    left_eye_y REAL NOT NULL,
+                    right_eye_x REAL NOT NULL,
+                    right_eye_y REAL NOT NULL,
+                    nose_x REAL NOT NULL,
+                    nose_y REAL NOT NULL,
+                    left_mouth_x REAL NOT NULL,
+                    left_mouth_y REAL NOT NULL,
+                    right_mouth_x REAL NOT NULL,
+                    right_mouth_y REAL NOT NULL,
+                    FOREIGN KEY (face_id) REFERENCES faces (id) ON DELETE CASCADE
+                )
+                "#,
+                [],
+            )
+            .change_context(Error)?;
+
+        // Embeddings table
+        self.conn
+            .execute(
+                r#"
+                CREATE TABLE IF NOT EXISTS embeddings (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    face_id INTEGER NOT NULL,
+                    embedding BLOB NOT NULL,
+                    model_name TEXT NOT NULL,
+                    created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
+                    FOREIGN KEY (face_id) REFERENCES faces (id) ON DELETE CASCADE
+                )
+                "#,
+                [],
+            )
+            .change_context(Error)?;
+
+        // Create indexes for better performance
+        self.conn
+            .execute(
+                "CREATE INDEX IF NOT EXISTS idx_faces_image_id ON faces (image_id)",
+                [],
+            )
+            .change_context(Error)?;
+
+        self.conn
+            .execute(
+                "CREATE INDEX IF NOT EXISTS idx_landmarks_face_id ON landmarks (face_id)",
+                [],
+            )
+            .change_context(Error)?;
+
+        self.conn
+            .execute(
+                "CREATE INDEX IF NOT EXISTS idx_embeddings_face_id ON embeddings (face_id)",
+                [],
+            )
+            .change_context(Error)?;
+
+        Ok(())
+    }
+
+    /// Store image metadata and return the image ID
+    pub fn store_image(&self, file_path: &str, width: u32, height: u32) -> Result<i64> {
+        let mut stmt = self
+            .conn
+            .prepare("INSERT OR REPLACE INTO images (file_path, width, height) VALUES (?1, ?2, ?3)")
+            .change_context(Error)?;
+
+        stmt.execute(params![file_path, width, height])
+            .change_context(Error)?;
+
+        Ok(self.conn.last_insert_rowid())
+    }
+
+    /// Store face detection results
+    pub fn store_face_detections(
+        &self,
+        image_id: i64,
+        detection_output: &FaceDetectionOutput,
+    ) -> Result<Vec<i64>> {
+        let mut face_ids = Vec::new();
+
+        for (i, bbox) in detection_output.bbox.iter().enumerate() {
+            let confidence = detection_output.confidence.get(i).copied().unwrap_or(0.0);
+
+            let face_id = self.store_face(image_id, bbox, confidence)?;
+            face_ids.push(face_id);
+
+            // Store landmarks if available
+            if let Some(landmarks) = detection_output.landmark.get(i) {
+                self.store_landmarks(face_id, landmarks)?;
+            }
+        }
+
+        Ok(face_ids)
+    }
+
+    /// Store a single face detection
+    pub fn store_face(&self, image_id: i64, bbox: &Aabb2<usize>, confidence: f32) -> Result<i64> {
+        let mut stmt = self
+            .conn
+            .prepare(
+                r#"
+                INSERT INTO faces (image_id, bbox_x1, bbox_y1, bbox_x2, bbox_y2, confidence)
+                VALUES (?1, ?2, ?3, ?4, ?5, ?6)
+                "#,
+            )
+            .change_context(Error)?;
+
+        stmt.execute(params![
+            image_id,
+            bbox.x1() as f32,
+            bbox.y1() as f32,
+            bbox.x2() as f32,
+            bbox.y2() as f32,
+            confidence
+        ])
+        .change_context(Error)?;
+
+        Ok(self.conn.last_insert_rowid())
+    }
+
+    /// Store face landmarks
+    pub fn store_landmarks(&self, face_id: i64, landmarks: &FaceLandmarks) -> Result<i64> {
+        let mut stmt = self
+            .conn
+            .prepare(
+                r#"
+                INSERT INTO landmarks
+                (face_id, left_eye_x, left_eye_y, right_eye_x, right_eye_y,
+                 nose_x, nose_y, left_mouth_x, left_mouth_y, right_mouth_x, right_mouth_y)
+                VALUES (?1, ?2, ?3, ?4, ?5, ?6, ?7, ?8, ?9, ?10, ?11)
+                "#,
+            )
+            .change_context(Error)?;
+
+        stmt.execute(params![
+            face_id,
+            landmarks.left_eye.x,
+            landmarks.left_eye.y,
+            landmarks.right_eye.x,
+            landmarks.right_eye.y,
+            landmarks.nose.x,
+            landmarks.nose.y,
+            landmarks.left_mouth.x,
+            landmarks.left_mouth.y,
+            landmarks.right_mouth.x,
+            landmarks.right_mouth.y,
+        ])
+        .change_context(Error)?;
+
+        Ok(self.conn.last_insert_rowid())
+    }
+
+    /// Store face embeddings
+    pub fn store_embeddings(
+        &self,
+        face_ids: &[i64],
+        embeddings: &[ndarray::Array2<f32>],
+        model_name: &str,
+    ) -> Result<Vec<i64>> {
+        let mut embedding_ids = Vec::new();
+
+        for (face_idx, embedding_batch) in embeddings.iter().enumerate() {
+            for (batch_idx, embedding_row) in embedding_batch.rows().into_iter().enumerate() {
+                let global_idx = face_idx * embedding_batch.nrows() + batch_idx;
+
+                if global_idx >= face_ids.len() {
+                    break;
+                }
+
+                let face_id = face_ids[global_idx];
+                let embedding_id =
+                    self.store_single_embedding(face_id, embedding_row, model_name)?;
+                embedding_ids.push(embedding_id);
+            }
+        }
+
+        Ok(embedding_ids)
+    }
+
+    /// Store a single embedding
+    pub fn store_single_embedding(
+        &self,
+        face_id: i64,
+        embedding: ndarray::ArrayView1<f32>,
+        model_name: &str,
+    ) -> Result<i64> {
+        let embedding_bytes =
+            ndarray_safetensors::SafeArrays::from_ndarrays([("embedding", embedding)])
+                .change_context(Error)?
+                .serialize()
+                .change_context(Error)?;
+
+        let mut stmt = self
+            .conn
+            .prepare("INSERT INTO embeddings (face_id, embedding, model_name) VALUES (?1, ?2, ?3)")
+            .change_context(Error)?;
+
+        stmt.execute(params![face_id, embedding_bytes, model_name])
+            .change_context(Error)?;
+
+        Ok(self.conn.last_insert_rowid())
+    }
+
+    /// Get image by ID
+    pub fn get_image(&self, image_id: i64) -> Result<Option<ImageRecord>> {
+        let mut stmt = self
+            .conn
+            .prepare("SELECT id, file_path, width, height, created_at FROM images WHERE id = ?1")
+            .change_context(Error)?;
+
+        let result = stmt
+            .query_row(params![image_id], |row| {
+                Ok(ImageRecord {
+                    id: row.get(0)?,
+                    file_path: row.get(1)?,
+                    width: row.get(2)?,
+                    height: row.get(3)?,
+                    created_at: row.get(4)?,
+                })
+            })
+            .optional()
+            .change_context(Error)?;
+
+        Ok(result)
+    }
+
+    /// Get all faces for an image
+    pub fn get_faces_for_image(&self, image_id: i64) -> Result<Vec<FaceRecord>> {
+        let mut stmt = self
+            .conn
+            .prepare(
+                r#"
+                SELECT id, image_id, bbox_x1, bbox_y1, bbox_x2, bbox_y2, confidence, created_at
+                FROM faces WHERE image_id = ?1
+                "#,
+            )
+            .change_context(Error)?;
+
+        let face_iter = stmt
+            .query_map(params![image_id], |row| {
+                Ok(FaceRecord {
+                    id: row.get(0)?,
+                    image_id: row.get(1)?,
+                    bbox_x1: row.get(2)?,
+                    bbox_y1: row.get(3)?,
+                    bbox_x2: row.get(4)?,
+                    bbox_y2: row.get(5)?,
+                    confidence: row.get(6)?,
+                    created_at: row.get(7)?,
+                })
+            })
+            .change_context(Error)?;
+
+        let mut faces = Vec::new();
+        for face in face_iter {
+            faces.push(face.change_context(Error)?);
+        }
+
+        Ok(faces)
+    }
+
+    /// Get landmarks for a face
+    pub fn get_landmarks(&self, face_id: i64) -> Result<Option<LandmarkRecord>> {
+        let mut stmt = self
+            .conn
+            .prepare(
+                r#"
+                SELECT id, face_id, left_eye_x, left_eye_y, right_eye_x, right_eye_y,
+                       nose_x, nose_y, left_mouth_x, left_mouth_y, right_mouth_x, right_mouth_y
+                FROM landmarks WHERE face_id = ?1
+                "#,
+            )
+            .change_context(Error)?;
+
+        let result = stmt
+            .query_row(params![face_id], |row| {
+                Ok(LandmarkRecord {
+                    id: row.get(0)?,
+                    face_id: row.get(1)?,
+                    left_eye_x: row.get(2)?,
+                    left_eye_y: row.get(3)?,
+                    right_eye_x: row.get(4)?,
+                    right_eye_y: row.get(5)?,
+                    nose_x: row.get(6)?,
+                    nose_y: row.get(7)?,
+                    left_mouth_x: row.get(8)?,
+                    left_mouth_y: row.get(9)?,
+                    right_mouth_x: row.get(10)?,
+                    right_mouth_y: row.get(11)?,
+                })
+            })
+            .optional()
+            .change_context(Error)?;
+
+        Ok(result)
+    }
+
+    /// Get embeddings for a face
+    pub fn get_embeddings(&self, face_id: i64) -> Result<Vec<EmbeddingRecord>> {
+        let mut stmt = self
+            .conn
+            .prepare(
+                "SELECT id, face_id, embedding, model_name, created_at FROM embeddings WHERE face_id = ?1",
+            )
+            .change_context(Error)?;
+
+        let embedding_iter = stmt
+            .query_map(params![face_id], |row| {
+                let embedding_bytes: Vec<u8> = row.get(2)?;
+                let embedding: ndarray::Array1<f32> = {
+                    let sf = ndarray_safetensors::SafeArraysView::from_bytes(&embedding_bytes)
+                        .change_context(Error)
+                        // .change_context(Error)?
+                        .unwrap();
+                    sf.tensor::<f32, ndarray::Ix1>("embedding")
+                        // .change_context(Error)?
+                        .unwrap()
+                        .to_owned()
+                };
+
+                Ok(EmbeddingRecord {
+                    id: row.get(0)?,
+                    face_id: row.get(1)?,
+                    embedding,
+                    model_name: row.get(3)?,
+                    created_at: row.get(4)?,
+                })
+            })
+            .change_context(Error)?;
+
+        let mut embeddings = Vec::new();
+        for embedding in embedding_iter {
+            embeddings.push(embedding.change_context(Error)?);
+        }
+
+        Ok(embeddings)
+    }
+
+    /// Get database statistics
+    pub fn get_stats(&self) -> Result<(usize, usize, usize, usize)> {
+        let images: usize = self
+            .conn
+            .query_row("SELECT COUNT(*) FROM images", [], |row| row.get(0))
+            .change_context(Error)?;
+
+        let faces: usize = self
+            .conn
+            .query_row("SELECT COUNT(*) FROM faces", [], |row| row.get(0))
+            .change_context(Error)?;
+
+        let landmarks: usize = self
+            .conn
+            .query_row("SELECT COUNT(*) FROM landmarks", [], |row| row.get(0))
+            .change_context(Error)?;
+
+        let embeddings: usize = self
+            .conn
+            .query_row("SELECT COUNT(*) FROM embeddings", [], |row| row.get(0))
+            .change_context(Error)?;
+
+        Ok((images, faces, landmarks, embeddings))
+    }
+
+    /// Find similar faces based on cosine similarity of embeddings
+    /// Return ids and similarity scores of similar faces
+    pub fn find_similar_faces(
+        &self,
+        embedding: &ndarray::Array1<f32>,
+        threshold: f32,
+        limit: usize,
+    ) -> Result<Vec<(i64, f32)>> {
+        // Serialize the query embedding to bytes
+        let embedding_bytes =
+            ndarray_safetensors::SafeArrays::from_ndarrays([("embedding", embedding.view())])
+                .change_context(Error)?
+                .serialize()
+                .change_context(Error)?;
+
+        let mut stmt = self
+            .conn
+            .prepare(
+                r#" SELECT face_id, cosine_similarity(?1, embedding) as similarity
+                    FROM embeddings
+                    WHERE cosine_similarity(?1, embedding) >= ?2
+                    ORDER BY similarity DESC
+                    LIMIT ?3"#,
+            )
+            .change_context(Error)?;
+
+        let result = stmt
+            .query_map(params![embedding_bytes, threshold, limit], |row| {
+                Ok((row.get::<_, i64>(0)?, row.get::<_, f32>(1)?))
+            })
+            .change_context(Error)?
+            .map(|r| r.change_context(Error))
+            .collect::<Result<Vec<_>>>()?;
+
+        // let mut results = Vec::new();
+        // for result in result_iter {
+        //     results.push(result.change_context(Error)?);
+        // }
+
+        Ok(result)
+    }
+}
+
+fn add_sqlite_cosine_similarity(db: &Connection) -> Result<()> {
+    use rusqlite::functions::*;
+    db.create_scalar_function(
+        "cosine_similarity",
+        2,
+        FunctionFlags::SQLITE_UTF8 | FunctionFlags::SQLITE_DETERMINISTIC,
+        move |ctx| {
+            if ctx.len() != 2 {
+                return Err(rusqlite::Error::UserFunctionError(
+                    "cosine_similarity requires exactly 2 arguments".into(),
+                ));
+            }
+            let array_1 = ctx.get_raw(0).as_blob()?;
+            let array_2 = ctx.get_raw(1).as_blob()?;
+
+            let array_1_st = ndarray_safetensors::SafeArraysView::from_bytes(array_1)
+                .map_err(|e| rusqlite::Error::UserFunctionError(e.into()))?;
+            let array_2_st = ndarray_safetensors::SafeArraysView::from_bytes(array_2)
+                .map_err(|e| rusqlite::Error::UserFunctionError(e.into()))?;
+
+            let array_view_1 = array_1_st
+                .tensor::<f32, ndarray::Ix1>("embedding")
+                .map_err(|e| rusqlite::Error::UserFunctionError(e.into()))?;
+            let array_view_2 = array_2_st
+                .tensor::<f32, ndarray::Ix1>("embedding")
+                .map_err(|e| rusqlite::Error::UserFunctionError(e.into()))?;
+
+            let similarity = array_view_1
+                .cosine_similarity(array_view_2)
+                .map_err(|e| rusqlite::Error::UserFunctionError(e.into()))?;
+
+            Ok(similarity)
+        },
+    )
+    .change_context(Error)
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_database_creation() -> Result<()> {
+        let db = FaceDatabase::in_memory()?;
+        let (images, faces, landmarks, embeddings) = db.get_stats()?;
+        assert_eq!(images, 0);
+        assert_eq!(faces, 0);
+        assert_eq!(landmarks, 0);
+        assert_eq!(embeddings, 0);
+        Ok(())
+    }
+
+    #[test]
+    fn test_store_and_retrieve_image() -> Result<()> {
+        let db = FaceDatabase::in_memory()?;
+        let image_id = db.store_image("/path/to/image.jpg", 800, 600)?;
+
+        let image = db.get_image(image_id)?.unwrap();
+        assert_eq!(image.file_path, "/path/to/image.jpg");
+        assert_eq!(image.width, 800);
+        assert_eq!(image.height, 600);
+
+        Ok(())
+    }
+}

src/lib.rs

@@ -1,3 +1,4 @@
+pub mod database;
 pub mod errors;
 pub mod facedet;
 pub mod faceembed;

src/main.rs

@@ -1,9 +1,10 @@
 mod cli;
 mod errors;
 use bounding_box::roi::MultiRoi;
-use detector::{facedet, facedet::FaceDetectionConfig, faceembed};
+use detector::{database::FaceDatabase, facedet, facedet::FaceDetectionConfig, faceembed};
 use errors::*;
 use fast_image_resize::ResizeOptions;
+
 use ndarray::*;
 use ndarray_image::*;
 use ndarray_resize::NdFir;
@@ -13,7 +14,7 @@ const RETINAFACE_MODEL_ONNX: &[u8] = include_bytes!("../models/retinaface.onnx")
 const FACENET_MODEL_ONNX: &[u8] = include_bytes!("../models/facenet.onnx");
 pub fn main() -> Result<()> {
     tracing_subscriber::fmt()
-        .with_env_filter("error")
+        .with_env_filter("info")
         .with_thread_ids(true)
         .with_thread_names(true)
         .with_target(false)
@@ -77,6 +78,15 @@ pub fn main() -> Result<()> {
         cli::SubCommand::List(list) => {
             println!("List: {:?}", list);
         }
+        cli::SubCommand::Query(query) => {
+            run_query(query)?;
+        }
+        cli::SubCommand::Similar(similar) => {
+            run_similar(similar)?;
+        }
+        cli::SubCommand::Stats(stats) => {
+            run_stats(stats)?;
+        }
         cli::SubCommand::Completions { shell } => {
             cli::Cli::completions(shell);
         }
@@ -89,10 +99,22 @@ where
     D: facedet::FaceDetector,
     E: faceembed::FaceEmbedder,
 {
+    // Initialize database if requested
+    let db = if detect.save_to_db {
+        let db_path = detect
+            .database
+            .as_ref()
+            .map(|p| p.as_path())
+            .unwrap_or_else(|| std::path::Path::new("face_detections.db"));
+        Some(FaceDatabase::new(db_path).change_context(Error)?)
+    } else {
+        None
+    };
     let image = image::open(&detect.image)
         .change_context(Error)
         .attach_printable(detect.image.to_string_lossy().to_string())?;
     let image = image.into_rgb8();
+    let (image_width, image_height) = image.dimensions();
     let mut array = image
         .into_ndarray()
         .change_context(errors::Error)
@@ -106,6 +128,26 @@ where
         )
         .change_context(errors::Error)
         .attach_printable("Failed to detect faces")?;
+
+    // Store image and face detections in database if requested
+    let (image_id, face_ids) = if let Some(ref database) = db {
+        let image_path = detect.image.to_string_lossy();
+        let img_id = database
+            .store_image(&image_path, image_width, image_height)
+            .change_context(Error)?;
+        let face_ids = database
+            .store_face_detections(img_id, &output)
+            .change_context(Error)?;
+        tracing::info!(
+            "Stored image {} with {} faces in database",
+            img_id,
+            face_ids.len()
+        );
+        (Some(img_id), Some(face_ids))
+    } else {
+        (None, None)
+    };
+
     for bbox in &output.bbox {
         tracing::info!("Detected face: {:?}", bbox);
         use bounding_box::draw::*;
@@ -159,6 +201,25 @@ where
         })
         .collect::<Result<Vec<Array2<f32>>>>()?;
 
+    // Store embeddings in database if requested
+    if let (Some(database), Some(face_ids)) = (&db, &face_ids) {
+        let embedding_ids = database
+            .store_embeddings(face_ids, &embeddings, &detect.model_name)
+            .change_context(Error)?;
+        tracing::info!("Stored {} embeddings in database", embedding_ids.len());
+
+        // Print database statistics
+        let (num_images, num_faces, num_landmarks, num_embeddings) =
+            database.get_stats().change_context(Error)?;
+        tracing::info!(
+            "Database stats - Images: {}, Faces: {}, Landmarks: {}, Embeddings: {}",
+            num_images,
+            num_faces,
+            num_landmarks,
+            num_embeddings
+        );
+    }
+
     let v = array.view();
     if let Some(output) = detect.output {
         let image: image::RgbImage = v
@@ -173,3 +234,135 @@ where
 
     Ok(())
 }
+
+fn run_query(query: cli::Query) -> Result<()> {
+    let db = FaceDatabase::new(&query.database).change_context(Error)?;
+
+    if let Some(image_id) = query.image_id {
+        if let Some(image) = db.get_image(image_id).change_context(Error)? {
+            println!("Image: {}", image.file_path);
+            println!("Dimensions: {}x{}", image.width, image.height);
+            println!("Created: {}", image.created_at);
+
+            let faces = db.get_faces_for_image(image_id).change_context(Error)?;
+            println!("Faces found: {}", faces.len());
+
+            for face in faces {
+                println!(
+                    "  Face ID {}: bbox({:.1}, {:.1}, {:.1}, {:.1}), confidence: {:.3}",
+                    face.id,
+                    face.bbox_x1,
+                    face.bbox_y1,
+                    face.bbox_x2,
+                    face.bbox_y2,
+                    face.confidence
+                );
+
+                if query.show_landmarks {
+                    if let Some(landmarks) = db.get_landmarks(face.id).change_context(Error)? {
+                        println!(
+                            "    Landmarks: left_eye({:.1}, {:.1}), right_eye({:.1}, {:.1}), nose({:.1}, {:.1})",
+                            landmarks.left_eye_x,
+                            landmarks.left_eye_y,
+                            landmarks.right_eye_x,
+                            landmarks.right_eye_y,
+                            landmarks.nose_x,
+                            landmarks.nose_y
+                        );
+                    }
+                }
+
+                if query.show_embeddings {
+                    let embeddings = db.get_embeddings(face.id).change_context(Error)?;
+                    for embedding in embeddings {
+                        println!(
+                            "    Embedding ({}): {} dims, model: {}",
+                            embedding.id,
+                            embedding.embedding.len(),
+                            embedding.model_name
+                        );
+                    }
+                }
+            }
+        } else {
+            println!("Image with ID {} not found", image_id);
+        }
+    }
+
+    if let Some(face_id) = query.face_id {
+        if let Some(landmarks) = db.get_landmarks(face_id).change_context(Error)? {
+            println!(
+                "Landmarks for face {}: left_eye({:.1}, {:.1}), right_eye({:.1}, {:.1}), nose({:.1}, {:.1})",
+                face_id,
+                landmarks.left_eye_x,
+                landmarks.left_eye_y,
+                landmarks.right_eye_x,
+                landmarks.right_eye_y,
+                landmarks.nose_x,
+                landmarks.nose_y
+            );
+        } else {
+            println!("No landmarks found for face {}", face_id);
+        }
+
+        let embeddings = db.get_embeddings(face_id).change_context(Error)?;
+        println!(
+            "Embeddings for face {}: {} found",
+            face_id,
+            embeddings.len()
+        );
+        for embedding in embeddings {
+            println!(
+                "  Embedding {}: {} dims, model: {}, created: {}",
+                embedding.id,
+                embedding.embedding.len(),
+                embedding.model_name,
+                embedding.created_at
+            );
+            // if query.show_embeddings {
+            //     println!("    Values: {:?}", &embedding.embedding);
+            // }
+        }
+    }
+
+    Ok(())
+}
+
+fn run_similar(similar: cli::Similar) -> Result<()> {
+    let db = FaceDatabase::new(&similar.database).change_context(Error)?;
+
+    let embeddings = db.get_embeddings(similar.face_id).change_context(Error)?;
+    if embeddings.is_empty() {
+        println!("No embeddings found for face {}", similar.face_id);
+        return Ok(());
+    }
+
+    let query_embedding = &embeddings[0].embedding;
+    let similar_faces = db
+        .find_similar_faces(query_embedding, similar.threshold, similar.limit)
+        .change_context(Error)?;
+
+    println!(
+        "Found {} similar faces (threshold: {:.3}):",
+        similar_faces.len(),
+        similar.threshold
+    );
+    for (face_id, similarity) in similar_faces {
+        println!("  Face {}: similarity {:.3}", face_id, similarity);
+    }
+
+    Ok(())
+}
+
+fn run_stats(stats: cli::Stats) -> Result<()> {
+    let db = FaceDatabase::new(&stats.database).change_context(Error)?;
+    let (images, faces, landmarks, embeddings) = db.get_stats().change_context(Error)?;
+
+    println!("Database Statistics:");
+    println!("  Images: {}", images);
+    println!("  Faces: {}", faces);
+    println!("  Landmarks: {}", landmarks);
+    println!("  Embeddings: {}", embeddings);
+
+    Ok(())
+}