diff --git a/rationai/resources/models.py b/rationai/resources/models.py
index 47be222..f023cfd 100644
--- a/rationai/resources/models.py
+++ b/rationai/resources/models.py
@@ -1,9 +1,11 @@
+from typing import Any
+
 import lz4.frame
 import numpy as np
 from httpx import USE_CLIENT_DEFAULT
 from httpx._client import UseClientDefault
 from httpx._types import TimeoutTypes
-from numpy.typing import NDArray
+from numpy.typing import DTypeLike, NDArray
 from PIL.Image import Image
 
 from rationai._resource import APIResource, AsyncAPIResource
@@ -64,6 +66,30 @@ def segment_image(
             lz4.frame.decompress(response.content), dtype=np.float16
         ).reshape(-1, h, w)
 
+    def embed_image(
+        self,
+        model: str,
+        image: Image | NDArray[np.uint8],
+        output_dtype: DTypeLike = np.float32,
+        timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT,
+    ) -> NDArray[np.floating[Any]]:
+        """Compute an embedding vector for an image using the specified model.
+
+        Args:
+            model: The name of the model to use for embedding.
+            image: The image to embed. It must be uint8 RGB image.
+            output_dtype: Output numpy dtype for embeddings (e.g. np.float16, np.float32).
+            timeout: Optional timeout for the request.
+
+        Returns:
+            NDArray[np.floating[Any]]: The embedding vector as a 1-D numpy array.
+        """
+        data = image.tobytes()
+        compressed_data = lz4.frame.compress(data)
+        response = self._post(model, data=compressed_data, timeout=timeout)
+        response.raise_for_status()
+        return np.array(response.json(), dtype=output_dtype)
+
 
 class AsyncModels(AsyncAPIResource):
     async def classify_image(
@@ -119,3 +145,27 @@ async def segment_image(
         return np.frombuffer(
             lz4.frame.decompress(response.content), dtype=np.float16
         ).reshape(-1, h, w)
+
+    async def embed_image(
+        self,
+        model: str,
+        image: Image | NDArray[np.uint8],
+        output_dtype: DTypeLike = np.float32,
+        timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT,
+    ) -> NDArray[np.floating[Any]]:
+        """Compute an embedding vector for an image using the specified model.
+
+        Args:
+            model: The name of the model to use for embedding.
+            image: The image to embed. It must be uint8 RGB image.
+            output_dtype: Output numpy dtype for embeddings (e.g. np.float16, np.float32).
+            timeout: Optional timeout for the request.
+
+        Returns:
+            NDArray[np.floating[Any]]: The embedding vector as a 1-D numpy array.
+        """
+        data = image.tobytes()
+        compressed_data = lz4.frame.compress(data)
+        response = await self._post(model, data=compressed_data, timeout=timeout)
+        response.raise_for_status()
+        return np.array(response.json(), dtype=output_dtype)