diff --git a/poetry.lock b/poetry.lock
index 4781d5dc46594c3179713882143f1b552e4956af..b2e36632ac1b7531c7924918a21b91fda4a513c5 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -449,13 +449,13 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [[package]]
 name = "cohere"
-version = "4.45"
+version = "4.47"
 description = "Python SDK for the Cohere API"
 optional = false
 python-versions = ">=3.8,<4.0"
 files = [
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-    {file = "cohere-4.45.tar.gz", hash = "sha256:63b21b2dc3abd718b18cae726a69d1b096a34eb59f3331c20469fd0df1672816"},
+    {file = "cohere-4.47-py3-none-any.whl", hash = "sha256:8b96a1ab57713eceffb4ffd58bf647055676fb741a4e5153c14296f9814c77fa"},
+    {file = "cohere-4.47.tar.gz", hash = "sha256:d689212079c1a545bd497b143cff7aa5632831fa6cf41bf7acc93b4a6effb839"},
 ]
 
 [package.dependencies]
@@ -597,29 +597,33 @@ toml = ["tomli"]
 
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 name = "debugpy"
-version = "1.8.0"
+version = "1.8.1"
 description = "An implementation of the Debug Adapter Protocol for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
@@ -699,42 +703,42 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth
 
 [[package]]
 name = "fastavro"
-version = "1.9.3"
+version = "1.9.4"
 description = "Fast read/write of AVRO files"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -923,13 +927,13 @@ files = [
 
 [[package]]
 name = "httpcore"
-version = "1.0.2"
+version = "1.0.3"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -940,7 +944,7 @@ h11 = ">=0.13,<0.15"
 asyncio = ["anyio (>=4.0,<5.0)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
-trio = ["trio (>=0.22.0,<0.23.0)"]
+trio = ["trio (>=0.22.0,<0.24.0)"]
 
 [[package]]
 name = "httpx"
@@ -1216,12 +1220,12 @@ test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "llama-cpp-python"
-version = "0.2.39"
+version = "0.2.44"
 description = "Python bindings for the llama.cpp library"
 optional = true
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1763,12 +1767,12 @@ nvidia-nvjitlink-cu12 = "*"
 
 [[package]]
 name = "nvidia-nccl-cu12"
-version = "2.19.3"
+version = "2.18.1"
 description = "NVIDIA Collective Communication Library (NCCL) Runtime"
 optional = true
 python-versions = ">=3"
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 ]
 
 [[package]]
@@ -1878,13 +1882,13 @@ sympy = "*"
 
 [[package]]
 name = "openai"
-version = "1.11.1"
+version = "1.12.0"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.7.1"
 files = [
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 [package.dependencies]
@@ -1901,61 +1905,61 @@ datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
 
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-version = "3.9.13"
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 [[package]]
@@ -2009,15 +2013,100 @@ files = [
 [package.dependencies]
 ptyprocess = ">=0.5"
 
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 sympy = "*"
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-typing-extensions = ">=4.8.0"
+triton = {version = "2.1.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+typing-extensions = "*"
 
 [package.extras]
+dynamo = ["jinja2"]
 opt-einsum = ["opt-einsum (>=3.3)"]
-optree = ["optree (>=0.9.1)"]
+
+[[package]]
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+    {file = "torchvision-0.16.2-cp38-cp38-macosx_10_13_x86_64.whl", hash = "sha256:b82732dcf876a37c852772342aa6ee3480c03bb3e2a802ae109fc5f7e28d26e9"},
+    {file = "torchvision-0.16.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:4b065143d1a720fe8a9077fd4be35d491f98819ec80b3dbbc3ec64d0b707a906"},
+    {file = "torchvision-0.16.2-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:bc5f274e4ecd1b86062063cdf4fd385a1d39d147a3a2685fbbde9ff08bb720b8"},
+    {file = "torchvision-0.16.2-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:335959c43b371c0474af34c1ef2a52efdc7603c45700d29e4475eeb02984170c"},
+    {file = "torchvision-0.16.2-cp38-cp38-win_amd64.whl", hash = "sha256:7fd22d86e08eba321af70cad291020c2cdeac069b00ce88b923ca52e06174769"},
+    {file = "torchvision-0.16.2-cp39-cp39-macosx_10_13_x86_64.whl", hash = "sha256:56115268b37f0b75364e3654e47ad9abc66ac34c1f9e5e3dfa89a22d6a40017a"},
+    {file = "torchvision-0.16.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:82805f8445b094f9d1e770390ee6cc86855e89955e08ce34af2e2274fc0e5c45"},
+    {file = "torchvision-0.16.2-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:3f4bd5fcbc361476e2e78016636ac7d5509e59d9962521f06eb98e6803898182"},
+    {file = "torchvision-0.16.2-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:8199acdf8ab066a28b84a5b6f4d97b58976d9e164b1acc3a9d14fccfaf74bb3a"},
+    {file = "torchvision-0.16.2-cp39-cp39-win_amd64.whl", hash = "sha256:41dd4fa9f176d563fe9f1b9adef3b7e582cdfb60ce8c9bc51b094a025be687c9"},
+]
+
+[package.dependencies]
+numpy = "*"
+pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
+requests = "*"
+torch = "2.1.2"
+
+[package.extras]
+scipy = ["scipy"]
 
 [[package]]
 name = "tornado"
@@ -3154,13 +3265,13 @@ files = [
 
 [[package]]
 name = "tqdm"
-version = "4.66.1"
+version = "4.66.2"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "tqdm-4.66.1-py3-none-any.whl", hash = "sha256:d302b3c5b53d47bce91fea46679d9c3c6508cf6332229aa1e7d8653723793386"},
-    {file = "tqdm-4.66.1.tar.gz", hash = "sha256:d88e651f9db8d8551a62556d3cff9e3034274ca5d66e93197cf2490e2dcb69c7"},
+    {file = "tqdm-4.66.2-py3-none-any.whl", hash = "sha256:1ee4f8a893eb9bef51c6e35730cebf234d5d0b6bd112b0271e10ed7c24a02bd9"},
+    {file = "tqdm-4.66.2.tar.gz", hash = "sha256:6cd52cdf0fef0e0f543299cfc96fec90d7b8a7e88745f411ec33eb44d5ed3531"},
 ]
 
 [package.dependencies]
@@ -3257,26 +3368,28 @@ vision = ["Pillow (>=10.0.1,<=15.0)"]
 
 [[package]]
 name = "triton"
-version = "2.2.0"
+version = "2.1.0"
 description = "A language and compiler for custom Deep Learning operations"
 optional = true
 python-versions = "*"
 files = [
-    {file = "triton-2.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a2294514340cfe4e8f4f9e5c66c702744c4a117d25e618bd08469d0bfed1e2e5"},
-    {file = "triton-2.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:da58a152bddb62cafa9a857dd2bc1f886dbf9f9c90a2b5da82157cd2b34392b0"},
-    {file = "triton-2.2.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0af58716e721460a61886668b205963dc4d1e4ac20508cc3f623aef0d70283d5"},
-    {file = "triton-2.2.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e8fe46d3ab94a8103e291bd44c741cc294b91d1d81c1a2888254cbf7ff846dab"},
-    {file = "triton-2.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b8ce26093e539d727e7cf6f6f0d932b1ab0574dc02567e684377630d86723ace"},
-    {file = "triton-2.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:227cc6f357c5efcb357f3867ac2a8e7ecea2298cd4606a8ba1e931d1d5a947df"},
+    {file = "triton-2.1.0-0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:66439923a30d5d48399b08a9eae10370f6c261a5ec864a64983bae63152d39d7"},
+    {file = "triton-2.1.0-0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:919b06453f0033ea52c13eaf7833de0e57db3178d23d4e04f9fc71c4f2c32bf8"},
+    {file = "triton-2.1.0-0-cp37-cp37m-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ae4bb8a91de790e1866405211c4d618379781188f40d5c4c399766914e84cd94"},
+    {file = "triton-2.1.0-0-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:39f6fb6bdccb3e98f3152e3fbea724f1aeae7d749412bbb1fa9c441d474eba26"},
+    {file = "triton-2.1.0-0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:21544e522c02005a626c8ad63d39bdff2f31d41069592919ef281e964ed26446"},
+    {file = "triton-2.1.0-0-pp37-pypy37_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:143582ca31dd89cd982bd3bf53666bab1c7527d41e185f9e3d8a3051ce1b663b"},
+    {file = "triton-2.1.0-0-pp38-pypy38_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:82fc5aeeedf6e36be4e4530cbdcba81a09d65c18e02f52dc298696d45721f3bd"},
+    {file = "triton-2.1.0-0-pp39-pypy39_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:81a96d110a738ff63339fc892ded095b31bd0d205e3aace262af8400d40b6fa8"},
 ]
 
 [package.dependencies]
 filelock = "*"
 
 [package.extras]
-build = ["cmake (>=3.20)", "lit"]
-tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)", "torch"]
-tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
+build = ["cmake (>=3.18)", "lit"]
+tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)"]
+tutorials = ["matplotlib", "pandas", "tabulate"]
 
 [[package]]
 name = "types-pyyaml"
@@ -3291,13 +3404,13 @@ files = [
 
 [[package]]
 name = "types-requests"
-version = "2.31.0.20240125"
+version = "2.31.0.20240218"
 description = "Typing stubs for requests"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-requests-2.31.0.20240125.tar.gz", hash = "sha256:03a28ce1d7cd54199148e043b2079cdded22d6795d19a2c2a6791a4b2b5e2eb5"},
-    {file = "types_requests-2.31.0.20240125-py3-none-any.whl", hash = "sha256:9592a9a4cb92d6d75d9b491a41477272b710e021011a2a3061157e2fb1f1a5d1"},
+    {file = "types-requests-2.31.0.20240218.tar.gz", hash = "sha256:f1721dba8385958f504a5386240b92de4734e047a08a40751c1654d1ac3349c5"},
+    {file = "types_requests-2.31.0.20240218-py3-none-any.whl", hash = "sha256:a82807ec6ddce8f00fe0e949da6d6bc1fbf1715420218a9640d695f70a9e5a9b"},
 ]
 
 [package.dependencies]
@@ -3316,13 +3429,13 @@ files = [
 
 [[package]]
 name = "urllib3"
-version = "2.2.0"
+version = "2.2.1"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "urllib3-2.2.0-py3-none-any.whl", hash = "sha256:ce3711610ddce217e6d113a2732fafad960a03fd0318c91faa79481e35c11224"},
-    {file = "urllib3-2.2.0.tar.gz", hash = "sha256:051d961ad0c62a94e50ecf1af379c3aba230c66c710493493560c0c223c49f20"},
+    {file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
+    {file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
 ]
 
 [package.extras]
@@ -3473,10 +3586,10 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [extras]
 fastembed = ["fastembed"]
 hybrid = ["pinecone-text"]
-local = ["llama-cpp-python", "torch", "transformers"]
+local = ["llama-cpp-python", "torch", "torchvision", "transformers"]
 pinecone = ["pinecone-client"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.13"
-content-hash = "2489dc5d7e05f05d822b85b8f6dc063ea1cf7771d07408338741270f50300e9a"
+content-hash = "f599bf518d075937db3bf6bc3b7984fe0daab85b1f91fe1ce260080b4b69eba7"
diff --git a/pyproject.toml b/pyproject.toml
index 45ec2a9ecf1c00c6a0cc18c9431cc79cdb541778..18b469bc8ccb36eea75719494a1f975873cd32a4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -31,10 +31,12 @@ llama-cpp-python = {version = "^0.2.28", optional = true}
 black = "^23.12.1"
 colorama = "^0.4.6"
 pinecone-client = {version="^3.0.0", optional = true}
+torchvision = { version = "^0.16.2", optional = true}
+
 [tool.poetry.extras]
 hybrid = ["pinecone-text"]
 fastembed = ["fastembed"]
-local = ["torch", "transformers", "llama-cpp-python"]
+local = ["torch", "torchvision", "transformers", "llama-cpp-python"]
 pinecone = ["pinecone-client"]
 
 [tool.poetry.group.dev.dependencies]
diff --git a/semantic_router/encoders/__init__.py b/semantic_router/encoders/__init__.py
index 4f27e96334dd34bf215f0f416db84178bcdde6d9..9e74bb1a6a85388bf0f659348a32d76461df1b67 100644
--- a/semantic_router/encoders/__init__.py
+++ b/semantic_router/encoders/__init__.py
@@ -6,6 +6,7 @@ from semantic_router.encoders.huggingface import HuggingFaceEncoder
 from semantic_router.encoders.mistral import MistralEncoder
 from semantic_router.encoders.openai import OpenAIEncoder
 from semantic_router.encoders.tfidf import TfidfEncoder
+from semantic_router.encoders.vit import VitEncoder
 from semantic_router.encoders.zure import AzureOpenAIEncoder
 
 __all__ = [
@@ -18,4 +19,5 @@ __all__ = [
     "FastEmbedEncoder",
     "HuggingFaceEncoder",
     "MistralEncoder",
+    "VitEncoder",
 ]
diff --git a/semantic_router/encoders/vit.py b/semantic_router/encoders/vit.py
new file mode 100644
index 0000000000000000000000000000000000000000..23432c6f9939f204d50765be01bdf25a55e05617
--- /dev/null
+++ b/semantic_router/encoders/vit.py
@@ -0,0 +1,94 @@
+from typing import Any, List, Optional
+
+from pydantic.v1 import PrivateAttr
+
+from semantic_router.encoders import BaseEncoder
+
+
+class VitEncoder(BaseEncoder):
+    name: str = "google/vit-base-patch16-224"
+    type: str = "huggingface"
+    score_threshold: float = 0.5
+    extractor_kwargs: dict = {}
+    model_kwargs: dict = {}
+    device: Optional[str] = None
+    _extractor: Any = PrivateAttr()
+    _model: Any = PrivateAttr()
+    _torch: Any = PrivateAttr()
+    _T: Any = PrivateAttr()
+
+    def __init__(self, **data):
+        super().__init__(**data)
+        self._extractor, self._model = self._initialize_hf_model()
+
+    def _initialize_hf_model(self):
+        try:
+            from transformers import AutoFeatureExtractor, AutoModel
+        except ImportError:
+            raise ImportError(
+                "Please install transformers to use HuggingFaceEncoder. "
+                "You can install it with: "
+                "`pip install semantic-router[local]`"
+            )
+
+        try:
+            import torch
+            import torchvision.transforms as T
+        except ImportError:
+            raise ImportError(
+                "Please install Pytorch to use HuggingFaceEncoder. "
+                "You can install it with: "
+                "`pip install semantic-router[local]`"
+            )
+
+        self._torch = torch
+        self._T = T
+
+        extractor = AutoFeatureExtractor.from_pretrained(
+            self.name,
+            **self.extractor_kwargs,
+        )
+
+        model = AutoModel.from_pretrained(self.name, **self.model_kwargs)
+
+        if self.device:
+            model.to(self.device)
+
+        else:
+            device = "cuda" if self._torch.cuda.is_available() else "cpu"
+            model.to(device)
+            self.device = device
+
+        return extractor, model
+
+    def __call__(
+        self,
+        imgs: List[str],
+        batch_size: int = 32,
+    ) -> List[List[float]]:
+        all_embeddings = []
+        for i in range(0, len(imgs), batch_size):
+            batch_imgs = imgs[i : i + batch_size]
+            batch_imgs_transform = self._torch.stack(
+                [self._transformation_chain(img) for img in batch_imgs]
+            )
+            new_batch = {"pixel_values": batch_imgs_transform.to(self._model.device)}
+            with self._torch.no_grad():
+                embeddings = (
+                    self._model(**new_batch).last_hidden_state[:, 0].cpu().tolist()
+                )
+            all_embeddings.extend(embeddings)
+        return all_embeddings
+
+    def _transformation_chain(self, img):
+        return self._T.Compose(
+            [
+                # We first resize the input image to 256x256 and then we take center crop.
+                self._T.Resize(int((256 / 224) * self._extractor.size["height"])),
+                self._T.CenterCrop(self._extractor.size["height"]),
+                self._T.ToTensor(),
+                self._T.Normalize(
+                    mean=self._extractor.image_mean, std=self._extractor.image_std
+                ),
+            ]
+        )(img)
diff --git a/semantic_router/route.py b/semantic_router/route.py
index a1e0e9b0d23e622484792c90ab0fee1064ac01d9..d2e06668534384f5eeb223279a228ac98041cb77 100644
--- a/semantic_router/route.py
+++ b/semantic_router/route.py
@@ -40,7 +40,7 @@ def is_valid(route_config: str) -> bool:
 
 class Route(BaseModel):
     name: str
-    utterances: List[str]
+    utterances: Union[List[str], List[Any]]
     description: Optional[str] = None
     function_schema: Optional[Dict[str, Any]] = None
     llm: Optional[BaseLLM] = None
diff --git a/tests/unit/encoders/test_vit.py b/tests/unit/encoders/test_vit.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f123b8d3c1f413829b9ba3d073b4e8027b81eac
--- /dev/null
+++ b/tests/unit/encoders/test_vit.py
@@ -0,0 +1,56 @@
+import pytest
+
+from semantic_router.encoders import VitEncoder
+
+
+@pytest.fixture
+def mock_vit_encoder(mocker):
+    mocker.patch("transformers.AutoModel", autospec=True)
+    mocker.patch("transformers.AutoFeatureExtractor", autospec=True)
+
+    encoder = VitEncoder(name="some_model_name")
+    mocker.patch.object(encoder, "_initialize_hf_model")
+
+    return encoder
+
+
+class TestVitEncoder:
+    def test_vit_encoder__import_errors_transformers(self, mocker):
+        mocker.patch.dict("sys.modules", {"transformers": None})
+        with pytest.raises(ImportError):
+            VitEncoder()
+
+    def test_vit_encoder__import_errors_torch(self, mocker):
+        mocker.patch.dict("sys.modules", {"torch": None})
+        with pytest.raises(ImportError):
+            VitEncoder()
+
+    def test_vit_encoder__import_errors_torchvision(self, mocker):
+        mocker.patch.dict("sys.modules", {"torchvision": None})
+        with pytest.raises(ImportError):
+            VitEncoder()
+
+    @pytest.mark.skip(reason="TODO: Fix torch mocking")
+    def test_vit_encoder_initialization(self, mocker, monkeypatch):
+        mock_model = mocker.patch(
+            "transformers.AutoModel.from_pretrained", autospec=True
+        )
+        mock_extractor = mocker.patch(
+            "transformers.AutoFeatureExtractor.from_pretrained", autospec=True
+        )
+        monkeypatch.setattr(
+            "torch.cuda.is_available", mocker.MagicMock(return_value=False)
+        )
+
+        mock_model.return_value = mocker.MagicMock()
+        mock_extractor.return_value = mocker.MagicMock(size={"height": 224})
+
+        encoder = VitEncoder()
+
+        assert encoder.name == "google/vit-base-patch16-224"
+        assert encoder.type == "huggingface"
+        assert encoder.score_threshold == 0.5
+        assert encoder.device == "cpu"
+
+        mock_model.assert_called_once_with(encoder.name, **encoder.model_kwargs)
+        mock_extractor.assert_called_once_with(encoder.name, **encoder.extractor_kwargs)