diff --git a/libs/langchain/langchain/evaluation/embedding_distance/base.py b/libs/langchain/langchain/evaluation/embedding_distance/base.py
index 7fc0b0c66aa..9db1df4f1e4 100644
--- a/libs/langchain/langchain/evaluation/embedding_distance/base.py
+++ b/libs/langchain/langchain/evaluation/embedding_distance/base.py
@@ -3,7 +3,6 @@ from enum import Enum
 from typing import Any, Dict, List, Optional
 
 import numpy as np
-from langchain_community.embeddings.openai import OpenAIEmbeddings
 from langchain_core.callbacks.manager import (
     AsyncCallbackManagerForChainRun,
     CallbackManagerForChainRun,
@@ -18,6 +17,27 @@ from langchain.schema import RUN_KEY
 from langchain.utils.math import cosine_similarity
 
 
+def _embedding_factory() -> Embeddings:
+    """Create an Embeddings object.
+    Returns:
+        Embeddings: The created Embeddings object.
+    """
+    # Here for backwards compatibility.
+    # Generally, we do not want to be seeing imports from langchain community
+    # or partner packages in langchain.
+    try:
+        from langchain_openai import OpenAIEmbeddings
+    except ImportError:
+        try:
+            from langchain_community.embeddings.openai import OpenAIEmbeddings
+        except ImportError:
+            raise ImportError(
+                "Could not import OpenAIEmbeddings. Please install the "
+                "OpenAIEmbeddings package using `pip install langchain-openai`."
+            )
+    return OpenAIEmbeddings()
+
+
 class EmbeddingDistance(str, Enum):
     """Embedding Distance Metric.
 
@@ -45,7 +65,7 @@ class _EmbeddingDistanceChainMixin(Chain):
                                             for comparing the embeddings.
     """
 
-    embeddings: Embeddings = Field(default_factory=OpenAIEmbeddings)
+    embeddings: Embeddings = Field(default_factory=_embedding_factory)
     distance_metric: EmbeddingDistance = Field(default=EmbeddingDistance.COSINE)
 
     @root_validator(pre=False)
@@ -59,7 +79,28 @@ class _EmbeddingDistanceChainMixin(Chain):
             Dict[str, Any]: The validated values.
         """
         embeddings = values.get("embeddings")
-        if isinstance(embeddings, OpenAIEmbeddings):
+        types_ = []
+        try:
+            from langchain_openai import OpenAIEmbeddings
+
+            types_.append(OpenAIEmbeddings)
+        except ImportError:
+            pass
+
+        try:
+            from langchain_community.embeddings.openai import OpenAIEmbeddings
+
+            types_.append(OpenAIEmbeddings)
+        except ImportError:
+            pass
+
+        if not types_:
+            raise ImportError(
+                "Could not import OpenAIEmbeddings. Please install the "
+                "OpenAIEmbeddings package using `pip install langchain-openai`."
+            )
+
+        if isinstance(embeddings, tuple(types_)):
             try:
                 import tiktoken  # noqa: F401
             except ImportError: