diff --git a/docs/encoders/huggingface-endpoint.ipynb b/docs/encoders/huggingface-endpoint.ipynb
index c5714142..8f4c64a6 100644
--- a/docs/encoders/huggingface-endpoint.ipynb
+++ b/docs/encoders/huggingface-endpoint.ipynb
@@ -21,6 +21,17 @@
     "HuggingFace is a huge ecosystem of open source models. It can be run locally and supports the largest library of encoders."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Currently the Semantic Routeres `HFEndpointEncoder` class is setup to only use TEI (Text Imbedding Inference models). See: \n",
+    "\n",
+    "https://huggingface.co/docs/text-embeddings-inference/quick_tour\n",
+    "\n",
+    "For example, trying using `\"https://api-inference.huggingface.co/models/BAAI/bge-large-en-v1.5\"` as the `HF_API_URL`.\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -60,7 +71,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "d:\\Program_Installation\\anaconda\\envs\\testenv\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "c:\\Users\\Siraj\\Documents\\Personal\\Work\\Aurelio\\Virtual Environments\\semantic_router_3\\Lib\\site-packages\\pydantic\\_internal\\_config.py:341: UserWarning: Valid config keys have changed in V2:\n",
+      "* 'allow_population_by_field_name' has been renamed to 'populate_by_name'\n",
+      "* 'smart_union' has been removed\n",
+      "  warnings.warn(message, UserWarning)\n",
+      "c:\\Users\\Siraj\\Documents\\Personal\\Work\\Aurelio\\Virtual Environments\\semantic_router_3\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
       "  from .autonotebook import tqdm as notebook_tqdm\n"
      ]
     }
@@ -117,19 +132,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "d:\\Program_Installation\\anaconda\\envs\\testenv\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n",
-      "\u001b[32m2024-04-14 20:54:07 INFO semantic_router.utils.logger Model Initializing wait for - 26.81s \u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "from getpass import getpass\n",
@@ -146,3018 +151,1023 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing batch 1 with 3 documents\n"
+     ]
+    },
     {
      "data": {
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+       "  -0.03280803561210632,\n",
+       "  0.030909886583685875,\n",
+       "  -0.02676919661462307,\n",
+       "  -0.03910093009471893,\n",
+       "  0.006949405651539564,\n",
+       "  -0.04984179139137268,\n",
+       "  0.01013729814440012,\n",
+       "  0.021244961768388748,\n",
+       "  -0.028943194076418877,\n",
+       "  -0.040107931941747665,\n",
+       "  0.011134982109069824,\n",
+       "  0.04995227977633476,\n",
+       "  -0.009371100924909115,\n",
+       "  0.017319096252322197,\n",
+       "  -0.006938097532838583,\n",
+       "  0.025410644710063934,\n",
+       "  0.029393086209893227,\n",
+       "  0.04066159203648567,\n",
+       "  -0.011889754794538021,\n",
+       "  0.037195779383182526,\n",
+       "  0.019263794645667076,\n",
+       "  -0.0014912872575223446,\n",
+       "  -0.0029025778640061617,\n",
+       "  -0.03547181934118271,\n",
+       "  0.008915452286601067,\n",
+       "  -0.005287983454763889,\n",
+       "  -0.023488206788897514,\n",
+       "  -0.025507496669888496,\n",
+       "  0.006330494768917561,\n",
+       "  -0.02839474566280842,\n",
+       "  -0.022118978202342987,\n",
+       "  0.023928113281726837,\n",
+       "  0.02164549008011818,\n",
+       "  0.027446137741208076,\n",
+       "  -0.017917634919285774,\n",
+       "  -0.01735283061861992,\n",
+       "  -0.0435473769903183,\n",
+       "  -0.04275623708963394,\n",
+       "  -0.026344584301114082,\n",
+       "  -0.02356685884296894,\n",
+       "  0.06709715723991394,\n",
+       "  0.010336329229176044,\n",
+       "  0.013173925690352917,\n",
+       "  0.03969165310263634,\n",
+       "  -0.065531425178051,\n",
+       "  0.18516136705875397,\n",
+       "  0.043990347534418106,\n",
+       "  0.0655352994799614,\n",
+       "  0.013623605482280254,\n",
+       "  0.0009323807898908854,\n",
+       "  0.023029394447803497,\n",
+       "  -0.009624646976590157,\n",
+       "  0.008504793047904968,\n",
+       "  -0.046013347804546356,\n",
+       "  -0.03247398883104324,\n",
+       "  0.0015726988203823566,\n",
+       "  0.01353770773857832,\n",
+       "  0.027522603049874306,\n",
+       "  0.03272024542093277,\n",
+       "  0.029896369203925133,\n",
+       "  0.03771713376045227,\n",
+       "  -0.0005983465816825628,\n",
+       "  -0.010668111965060234,\n",
+       "  -0.02643754333257675,\n",
+       "  -0.035675935447216034,\n",
+       "  -0.03478347137570381,\n",
+       "  -0.0009089430677704513,\n",
+       "  0.013225086964666843,\n",
+       "  0.022539498284459114,\n",
+       "  0.025120986625552177,\n",
+       "  -0.0007197131053544581,\n",
+       "  0.03558710217475891,\n",
+       "  -0.012861058115959167,\n",
+       "  -0.018139783293008804,\n",
+       "  -0.038320064544677734,\n",
+       "  0.05922479182481766,\n",
+       "  0.013929803855717182,\n",
+       "  0.012118098326027393,\n",
+       "  -0.02312551438808441,\n",
+       "  -0.03430146351456642,\n",
+       "  0.040559470653533936,\n",
+       "  -0.0070445300079882145,\n",
+       "  -0.04097861796617508,\n",
+       "  0.0002990572829730809,\n",
+       "  0.02416178397834301,\n",
+       "  -0.03426935896277428,\n",
+       "  -0.03274112194776535,\n",
+       "  0.027410298585891724,\n",
+       "  0.002227137330919504,\n",
+       "  -0.01599731482565403,\n",
+       "  0.052870459854602814,\n",
+       "  -0.032784875482320786,\n",
+       "  0.03397718444466591,\n",
+       "  -0.019435621798038483,\n",
+       "  -0.028242681175470352,\n",
+       "  0.07161079347133636,\n",
+       "  -0.0490444153547287,\n",
+       "  0.03791863098740578,\n",
+       "  -0.05398724973201752,\n",
+       "  -0.05582103505730629,\n",
+       "  0.01960843615233898,\n",
+       "  0.02573646605014801,\n",
+       "  -0.023790620267391205,\n",
+       "  -0.022589296102523804,\n",
+       "  0.0008876558858901262,\n",
+       "  0.021006068214774132,\n",
+       "  0.00654869107529521,\n",
+       "  -0.007582558784633875,\n",
+       "  -0.0117470258846879,\n",
+       "  -0.053339045494794846,\n",
+       "  0.023703696206212044,\n",
+       "  0.066606804728508,\n",
+       "  0.02914503403007984,\n",
+       "  -0.02385387010872364,\n",
+       "  -0.004441776312887669,\n",
+       "  -0.037287961691617966,\n",
        "  ...]]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3175,14 +1185,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2024-04-14 20:02:32 INFO semantic_router.utils.logger local\u001b[0m\n"
+      "Processing batch 1 with 11 documents\n"
      ]
     }
    ],
@@ -3201,25 +1211,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "LocalIndex(index=array([[ 0.37762564,  0.37923592,  0.04006954, ...,  0.2910035 ,\n",
-       "         0.14261879, -0.14989774],\n",
-       "       [ 0.14489685, -0.47280183, -0.13473961, ..., -0.184137  ,\n",
-       "        -0.44280073, -0.96940869],\n",
-       "       [ 1.16709912,  0.38906148,  0.24399863, ...,  0.03619115,\n",
-       "        -0.00167309,  0.50425595],\n",
+       "LocalIndex(index=array([[ 0.02499899,  0.02385715,  0.00468016, ...,  0.01588508,\n",
+       "        -0.00209475, -0.01171774],\n",
+       "       [ 0.01642705, -0.02640625, -0.00659476, ..., -0.0194328 ,\n",
+       "        -0.02548762, -0.04860513],\n",
+       "       [ 0.06932217,  0.02093412,  0.01063143, ...,  0.00100745,\n",
+       "        -0.00822618,  0.03042692],\n",
        "       ...,\n",
-       "       [-0.64046752,  0.45156148, -0.27317011, ..., -0.64851284,\n",
-       "        -0.10258984,  0.15441738],\n",
-       "       [-0.11908327,  0.4233726 , -0.29102552, ..., -0.69622546,\n",
-       "         0.27602831,  0.2030668 ],\n",
-       "       [-0.06046702, -0.18556708, -0.45608515, ..., -0.86009502,\n",
-       "        -0.01424424, -0.489003  ]]), routes=array(['politics', 'politics', 'politics', 'politics', 'politics',\n",
+       "       [-0.0244599 ,  0.02428463, -0.01713768, ..., -0.0349757 ,\n",
+       "        -0.01013033,  0.01069217],\n",
+       "       [-0.00814284,  0.02359665, -0.02238297, ..., -0.03862419,\n",
+       "         0.00937595,  0.00824297],\n",
+       "       [-0.00102218, -0.00938131, -0.02210761, ..., -0.05303425,\n",
+       "        -0.00088396, -0.03256031]]), routes=array(['politics', 'politics', 'politics', 'politics', 'politics',\n",
        "       'politics', 'chitchat', 'chitchat', 'chitchat', 'chitchat',\n",
        "       'chitchat'], dtype='<U8'), utterances=array([\"isn't politics the best thing ever\",\n",
        "       \"why don't you tell me about your political opinions\",\n",
@@ -3229,10 +1239,10 @@
        "       'they will save the country!', \"how's the weather today?\",\n",
        "       'how are things going?', 'lovely weather today',\n",
        "       'the weather is horrendous', \"let's go to the chippy\"],\n",
-       "      dtype='<U51'), dimensions=None, type='local')"
+       "      dtype='<U51'), dimensions=None, type='local', init_async_index=False, sync=None)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3250,16 +1260,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing batch 1 with 1 documents\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "RouteChoice(name='politics', function_call=None, similarity_score=None)"
+       "RouteChoice(name=None, function_call=None, similarity_score=None)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3270,16 +1287,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing batch 1 with 1 documents\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "RouteChoice(name='chitchat', function_call=None, similarity_score=None)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3297,16 +1321,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing batch 1 with 1 documents\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "RouteChoice(name=None, function_call=None, similarity_score=None)"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3328,6 +1359,104 @@
    "source": [
     "---"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Testing Batches"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a larger list of documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_docs = [\n",
+    "    \"This is a test document about politics.\",\n",
+    "    \"The weather is nice today.\",\n",
+    "    \"I love discussing political issues.\",\n",
+    "    \"Let's talk about the latest news.\",\n",
+    "    \"What's your opinion on the current economic situation?\",\n",
+    "    \"How's the weather in your area?\",\n",
+    "    \"The political landscape is changing rapidly.\",\n",
+    "    \"I prefer to avoid political discussions.\",\n",
+    "    \"Do you think it will rain tomorrow?\",\n",
+    "    \"The government announced new policies today.\",\n",
+    "] * 10  # Repeat the list 10 times to get 100 documents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of input documents: 100\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Number of input documents: {len(test_docs)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's test the encoder with our larger list of documents:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing batch 1 with 50 documents\n",
+      "Processing batch 2 with 50 documents\n",
+      "Number of input documents: 100\n",
+      "Number of embeddings generated: 100\n",
+      "Dimension of each embedding: 1024\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize the encoder\n",
+    "encoder = HFEndpointEncoder(\n",
+    "    huggingface_url=huggingface_url,\n",
+    "    huggingface_api_key=huggingface_api_key,\n",
+    ")\n",
+    "\n",
+    "# Call the encoder with the test documents\n",
+    "embeddings = encoder(test_docs)\n",
+    "\n",
+    "# Print some information about the results\n",
+    "print(f\"Number of input documents: {len(test_docs)}\")\n",
+    "print(f\"Number of embeddings generated: {len(embeddings)}\")\n",
+    "print(f\"Dimension of each embedding: {len(embeddings[0])}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -3346,7 +1475,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/semantic_router/encoders/huggingface.py b/semantic_router/encoders/huggingface.py
index 7ca7580d..396e0240 100644
--- a/semantic_router/encoders/huggingface.py
+++ b/semantic_router/encoders/huggingface.py
@@ -211,18 +211,22 @@ def __call__(self, docs: List[str]) -> List[List[float]]:
         Raises:
             ValueError: If no embeddings are returned for a document.
         """
+
+        batch_size = 50
         embeddings = []
-        for d in docs:
+        for i in range(0, len(docs), batch_size):
+            batch = docs[i : i + batch_size]
             try:
-                output = self.query({"inputs": d, "parameters": {}})
-                if not output or len(output) == 0:
+                outputs = self.query({"inputs": batch, "parameters": {}})
+                if not outputs or len(outputs) == 0:
                     raise ValueError("No embeddings returned from the query.")
-                embeddings.append(output)
-
+                if isinstance(outputs[0], list):
+                    embeddings.extend(outputs)
+                else:
+                    embeddings.append(outputs)
             except Exception as e:
-                raise ValueError(
-                    f"No embeddings returned for document. Error: {e}"
-                ) from e
+                raise ValueError(f"No embeddings returned for batch. Error: {e}") from e
+
         return embeddings
 
     def query(self, payload, max_retries=3, retry_interval=5):
@@ -261,6 +265,7 @@ def query(self, payload, max_retries=3, retry_interval=5):
                         continue
                 else:
                     response.raise_for_status()
+                    break
 
             except requests.exceptions.RequestException:
                 if attempt < max_retries - 1:
diff --git a/tests/unit/encoders/test_hfendpointencoder.py b/tests/unit/encoders/test_hfendpointencoder.py
index cb8dd16a..65d6b361 100644
--- a/tests/unit/encoders/test_hfendpointencoder.py
+++ b/tests/unit/encoders/test_hfendpointencoder.py
@@ -1,5 +1,6 @@
 import pytest
 from semantic_router.encoders.huggingface import HFEndpointEncoder
+from unittest import mock
 
 
 @pytest.fixture
@@ -66,3 +67,76 @@ def test_encode_documents_success(self, encoder, requests_mock):
         )
         embeddings = encoder(["Hello World!"])
         assert embeddings == [[0.1, 0.2, 0.3]]
+
+    def test_initialization_failure_query_exception(self, requests_mock, mocker):
+        # Mock the query method to raise an exception
+        mocker.patch(
+            "semantic_router.encoders.huggingface.HFEndpointEncoder.query",
+            side_effect=Exception("Initialization error"),
+        )
+
+        with pytest.raises(ValueError) as exc_info:
+            HFEndpointEncoder(
+                huggingface_url="https://api-inference.huggingface.co/models/bert-base-uncased",
+                huggingface_api_key="test-api-key",
+            )
+        assert (
+            "HuggingFace endpoint client failed to initialize. Error: Initialization error"
+            in str(exc_info.value)
+        )
+
+    def test_no_embeddings_returned(self, encoder, requests_mock):
+        # Mock the response to return an empty list, simulating no embeddings
+        requests_mock.post(
+            "https://api-inference.huggingface.co/models/bert-base-uncased",
+            json=[],
+            status_code=200,
+        )
+        with pytest.raises(ValueError) as exc_info:
+            encoder(["Hello World!"])
+        assert "No embeddings returned from the query." in str(exc_info.value)
+
+    def test_no_embeddings_for_batch(self, encoder, requests_mock):
+        # Mock the response to simulate a server error
+        requests_mock.post(
+            "https://api-inference.huggingface.co/models/bert-base-uncased",
+            text="Error",
+            status_code=500,
+        )
+        with pytest.raises(ValueError) as exc_info:
+            encoder(["Hello World!"])
+        assert (
+            "No embeddings returned for batch. Error: Query failed with status 500: Error"
+            in str(exc_info.value)
+        )
+
+    def test_embeddings_extend(self, encoder, requests_mock):
+        # Mock the response to return a list of embeddings
+        requests_mock.post(
+            "https://api-inference.huggingface.co/models/bert-base-uncased",
+            json=[[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]],
+            status_code=200,
+        )
+        embeddings = encoder(["Hello World!", "Test"])
+        assert embeddings == [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]
+
+    def test_query_with_estimated_time(self, encoder, requests_mock):
+        # Mock the response to simulate a 503 status with an estimated_time
+        requests_mock.post(
+            "https://api-inference.huggingface.co/models/bert-base-uncased",
+            [
+                {
+                    "json": {"estimated_time": 2},
+                    "status_code": 503,
+                },
+                {
+                    "json": [0.1, 0.2, 0.3],
+                    "status_code": 200,
+                },
+            ],
+        )
+
+        with mock.patch("time.sleep", return_value=None) as mock_sleep:
+            response = encoder.query({"inputs": "Hello World!", "parameters": {}})
+            assert response == [0.1, 0.2, 0.3]
+            mock_sleep.assert_called_once_with(2)