diff --git a/langchain/embeddings/__init__.py b/langchain/embeddings/__init__.py
index b2d6091a35f..1967f6f3347 100644
--- a/langchain/embeddings/__init__.py
+++ b/langchain/embeddings/__init__.py
@@ -1,4 +1,6 @@
 """Wrappers around embedding modules."""
+from langchain.embeddings.cohere import CohereEmbeddings
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings
 from langchain.embeddings.openai import OpenAIEmbeddings
 
-__all__ = ["OpenAIEmbeddings"]
+__all__ = ["OpenAIEmbeddings", "HuggingFaceEmbeddings", "CohereEmbeddings"]
diff --git a/langchain/embeddings/cohere.py b/langchain/embeddings/cohere.py
new file mode 100644
index 00000000000..3ff641bfb02
--- /dev/null
+++ b/langchain/embeddings/cohere.py
@@ -0,0 +1,79 @@
+"""Wrapper around Cohere embedding models."""
+import os
+from typing import Any, Dict, List, Optional
+
+from pydantic import BaseModel, Extra, root_validator
+
+from langchain.embeddings.base import Embeddings
+
+
+class CohereEmbeddings(BaseModel, Embeddings):
+    """Wrapper around Cohere embedding models.
+
+    To use, you should have the ``cohere`` python package installed, and the
+    environment variable ``COHERE_API_KEY`` set with your API key or pass it
+    as a named parameter to the constructor.
+
+    Example:
+        .. code-block:: python
+
+            from langchain.embeddings import CohereEmbeddings
+            cohere = CohereEmbeddings(model_name="medium", cohere_api_key="my-api-key")
+    """
+
+    client: Any  #: :meta private:
+    model: str = "medium"
+    """Model name to use."""
+
+    cohere_api_key: Optional[str] = os.environ.get("COHERE_API_KEY")
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        cohere_api_key = values.get("cohere_api_key")
+
+        if cohere_api_key is None or cohere_api_key == "":
+            raise ValueError(
+                "Did not find Cohere API key, please add an environment variable"
+                " `COHERE_API_KEY` which contains it, or pass `cohere_api_key` as a"
+                " named parameter."
+            )
+        try:
+            import cohere
+
+            values["client"] = cohere.Client(cohere_api_key)
+        except ImportError:
+            raise ValueError(
+                "Could not import cohere python package. "
+                "Please it install it with `pip install cohere`."
+            )
+        return values
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Call out to Cohere's embedding endpoint.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        embeddings = self.client.embed(model=self.model, texts=texts).embeddings
+        return embeddings
+
+    def embed_query(self, text: str) -> List[float]:
+        """Call out to Cohere's embedding endpoint.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        embedding = self.client.embed(model=self.model, texts=[text]).embeddings[0]
+        return embedding
diff --git a/tests/integration_tests/embeddings/test_cohere.py b/tests/integration_tests/embeddings/test_cohere.py
new file mode 100644
index 00000000000..4e2aec50d23
--- /dev/null
+++ b/tests/integration_tests/embeddings/test_cohere.py
@@ -0,0 +1,19 @@
+"""Test cohere embeddings."""
+from langchain.embeddings.cohere import CohereEmbeddings
+
+
+def test_cohere_embedding_documents() -> None:
+    """Test cohere embeddings."""
+    documents = ["foo bar"]
+    embedding = CohereEmbeddings()
+    output = embedding.embed_documents(documents)
+    assert len(output) == 1
+    assert len(output[0]) == 2048
+
+
+def test_cohere_embedding_query() -> None:
+    """Test cohere embeddings."""
+    document = "foo bar"
+    embedding = CohereEmbeddings()
+    output = embedding.embed_query(document)
+    assert len(output) == 2048