diff --git a/README.md b/README.md
index 7b5c69e..f591aec 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
 
 The `milvus-model` library provides the integration with common embedding and reranker models for Milvus, a high performance open-source vector database built for AI applications.  `milvus-model` lib is included as a dependency in `pymilvus`, the Python SDK of Milvus.
 
-`milvus-model` supports embedding and reranker models from service providers like OpenAI, Voyage AI, Cohere, and open-source models through SentenceTransformers.
+`milvus-model` supports embedding and reranker models from service providers like OpenAI, Voyage AI, Cohere, and open-source models through SentenceTransformers or Hugging Face [Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference) .
 
 `milvus-model` supports Python 3.8 and above.
 
diff --git a/src/pymilvus/model/dense/__init__.py b/src/pymilvus/model/dense/__init__.py
index a8e91d5..f5c26bd 100644
--- a/src/pymilvus/model/dense/__init__.py
+++ b/src/pymilvus/model/dense/__init__.py
@@ -2,6 +2,7 @@
 from pymilvus.model.dense.sentence_transformer import SentenceTransformerEmbeddingFunction
 from pymilvus.model.dense.voyageai import VoyageEmbeddingFunction
 from pymilvus.model.dense.jinaai import JinaEmbeddingFunction
+from pymilvus.model.dense.tei import TEIEmbeddingFunction
 from pymilvus.model.dense.onnx import OnnxEmbeddingFunction
 from pymilvus.model.dense.cohere import CohereEmbeddingFunction
 from pymilvus.model.dense.mistralai import MistralAIEmbeddingFunction
@@ -13,9 +14,10 @@
     "SentenceTransformerEmbeddingFunction",
     "VoyageEmbeddingFunction",
     "JinaEmbeddingFunction",
+    "TEIEmbeddingFunction",
     "OnnxEmbeddingFunction",
     "CohereEmbeddingFunction",
     "MistralAIEmbeddingFunction",
     "NomicEmbeddingFunction",
-    "InstructorEmbeddingFunction"
+    "InstructorEmbeddingFunction",
 ]
diff --git a/src/pymilvus/model/dense/tei.py b/src/pymilvus/model/dense/tei.py
new file mode 100644
index 0000000..e69e1d6
--- /dev/null
+++ b/src/pymilvus/model/dense/tei.py
@@ -0,0 +1,49 @@
+from typing import List, Optional
+
+import numpy as np
+import requests
+
+from pymilvus.model.base import BaseEmbeddingFunction
+
+
+class TEIEmbeddingFunction(BaseEmbeddingFunction):
+    def __init__(
+        self,
+        api_url: str,
+        dimensions: Optional[int] = None,
+    ):
+        self.api_url = api_url + "/v1/embeddings"
+        self._session = requests.Session()
+        self._dim = dimensions
+
+    @property
+    def dim(self):
+        if self._dim is None:
+            # This works by sending a dummy message to the API to retrieve the vector dimension,
+            # as the original API does not directly provide this information
+            self._dim = self._call_api(["get dim"])[0].shape[0]
+        return self._dim
+
+    def encode_queries(self, queries: List[str]) -> List[np.array]:
+        return self._call_api(queries)
+
+    def encode_documents(self, documents: List[str]) -> List[np.array]:
+        return self._call_api(documents)
+
+    def __call__(self, texts: List[str]) -> List[np.array]:
+        return self._call_api(texts)
+
+    def _call_api(self, texts: List[str]):
+        data = {"input": texts}
+        resp = self._session.post(  # type: ignore[assignment]
+            self.api_url,
+            json=data,
+        ).json()
+        if "data" not in resp:
+            raise RuntimeError(resp["message"])
+
+        embeddings = resp["data"]
+
+        # Sort resulting embeddings by index
+        sorted_embeddings = sorted(embeddings, key=lambda e: e["index"])  # type: ignore[no-any-return]
+        return [np.array(result["embedding"]) for result in sorted_embeddings]
diff --git a/src/pymilvus/model/reranker/__init__.py b/src/pymilvus/model/reranker/__init__.py
index bd28640..7ac4f17 100644
--- a/src/pymilvus/model/reranker/__init__.py
+++ b/src/pymilvus/model/reranker/__init__.py
@@ -3,6 +3,7 @@
 from pymilvus.model.reranker.voyageai import VoyageRerankFunction
 from pymilvus.model.reranker.cross_encoder import CrossEncoderRerankFunction
 from pymilvus.model.reranker.jinaai import JinaRerankFunction
+from pymilvus.model.reranker.tei import TEIRerankFunction
 
 __all__ = [
     "CohereRerankFunction",
@@ -10,4 +11,5 @@
     "VoyageRerankFunction",
     "CrossEncoderRerankFunction",
     "JinaRerankFunction",
+    "TEIRerankFunction",
 ]
diff --git a/src/pymilvus/model/reranker/tei.py b/src/pymilvus/model/reranker/tei.py
new file mode 100644
index 0000000..1b81953
--- /dev/null
+++ b/src/pymilvus/model/reranker/tei.py
@@ -0,0 +1,28 @@
+from typing import List
+
+import requests
+
+from pymilvus.model.base import BaseRerankFunction, RerankResult
+
+
+class TEIRerankFunction(BaseRerankFunction):
+    def __init__(self, api_url: str):
+        self.api_url = api_url + "/rerank"
+        self._session = requests.Session()
+
+    def __call__(self, query: str, documents: List[str], top_k: int = 5) -> List[RerankResult]:
+        resp = self._session.post(  # type: ignore[assignment]
+            self.api_url,
+            json={
+                "query": query,
+                "return_text": True,
+                "texts": documents,
+            },
+        ).json()
+        if "error" in resp:
+            raise RuntimeError(resp["error"])
+
+        results = []
+        for res in resp[:5]:
+            results.append(RerankResult(text=res["text"], score=res["score"], index=res["index"]))
+        return results