diff --git a/libs/partners/openai/langchain_openai/embeddings/base.py b/libs/partners/openai/langchain_openai/embeddings/base.py
index 9e9ccb49c9e33..645242ac14594 100644
--- a/libs/partners/openai/langchain_openai/embeddings/base.py
+++ b/libs/partners/openai/langchain_openai/embeddings/base.py
@@ -101,87 +101,86 @@ def _process_batched_chunked_embeddings(
 class OpenAIEmbeddings(BaseModel, Embeddings):
     """OpenAI embedding model integration.
 
-    Setup:
-        Install ``langchain_openai`` and set environment variable ``OPENAI_API_KEY``.
-
-        .. code-block:: bash
-
-            pip install -U langchain_openai
-            export OPENAI_API_KEY="your-api-key"
-
-    Key init args — embedding params:
-        model: str
-            Name of OpenAI model to use.
-        dimensions: Optional[int] = None
-            The number of dimensions the resulting output embeddings should have.
-            Only supported in `text-embedding-3` and later models.
-
-   Key init args — client params:
-        api_key: Optional[SecretStr] = None
-            OpenAI API key.
-        organization: Optional[str] = None
-            OpenAI organization ID. If not passed in will be read
-            from env var OPENAI_ORG_ID.
-        max_retries: int = 2
-            Maximum number of retries to make when generating.
-        request_timeout: Optional[Union[float, Tuple[float, float], Any]] = None
-            Timeout for requests to OpenAI completion API
-
-    See full list of supported init args and their descriptions in the params section.
-
-    Instantiate:
-        .. code-block:: python
-
-            from langchain_openai import OpenAIEmbeddings
-
-            embed = OpenAIEmbeddings(
-                model="text-embedding-3-large",
-                # With the `text-embedding-3` class
-                # of models, you can specify the size
-                # of the embeddings you want returned.
-                # dimensions=1024
-            )
+     Setup:
+         Install ``langchain_openai`` and set environment variable ``OPENAI_API_KEY``.
 
-    Embed single text:
+         .. code-block:: bash
 
-        .. code-block:: python
+             pip install -U langchain_openai
+             export OPENAI_API_KEY="your-api-key"
 
-            input_text = "The meaning of life is 42"
-            vector = embeddings.embed_query('hello')
-            print(vector[:3])
+     Key init args — embedding params:
+         model: str
+             Name of OpenAI model to use.
+         dimensions: Optional[int] = None
+             The number of dimensions the resulting output embeddings should have.
+             Only supported in `text-embedding-3` and later models.
 
-        .. code-block:: python
+    Key init args — client params:
+         api_key: Optional[SecretStr] = None
+             OpenAI API key.
+         organization: Optional[str] = None
+             OpenAI organization ID. If not passed in will be read
+             from env var OPENAI_ORG_ID.
+         max_retries: int = 2
+             Maximum number of retries to make when generating.
+         request_timeout: Optional[Union[float, Tuple[float, float], Any]] = None
+             Timeout for requests to OpenAI completion API
 
-            [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
+     See full list of supported init args and their descriptions in the params section.
 
-    Embed multiple texts:
+     Instantiate:
+         .. code-block:: python
 
-        .. code-block:: python
+             from langchain_openai import OpenAIEmbeddings
 
-            vectors = embeddings.embed_documents(['hello', 'goodbye'])
-            # Showing only the first 3 coordinates
-            print(len(vectors))
-            print(vectors[0][:3])
+             embed = OpenAIEmbeddings(
+                 model="text-embedding-3-large"
+                 # With the `text-embedding-3` class
+                 # of models, you can specify the size
+                 # of the embeddings you want returned.
+                 # dimensions=1024
+             )
 
-        .. code-block:: python
+     Embed single text:
 
-            2
-            [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
+         .. code-block:: python
 
-    Async:
-        .. code-block:: python
+             input_text = "The meaning of life is 42"
+             vector = embeddings.embed_query("hello")
+             print(vector[:3])
 
-            await embed.aembed_query(input_text)
-            print(vector[:3])
+         .. code-block:: python
 
-            # multiple:
-            # await embed.aembed_documents(input_texts)
+             [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
 
-        .. code-block:: python
+     Embed multiple texts:
 
-            [-0.009100092574954033, 0.005071679595857859, -0.0029193938244134188]
-    """
+         .. code-block:: python
+
+             vectors = embeddings.embed_documents(["hello", "goodbye"])
+             # Showing only the first 3 coordinates
+             print(len(vectors))
+             print(vectors[0][:3])
+
+         .. code-block:: python
+
+             2
+             [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
 
+     Async:
+         .. code-block:: python
+
+             await embed.aembed_query(input_text)
+             print(vector[:3])
+
+             # multiple:
+             # await embed.aembed_documents(input_texts)
+
+         .. code-block:: python
+
+             [-0.009100092574954033, 0.005071679595857859, -0.0029193938244134188]
+    """
 
     client: Any = Field(default=None, exclude=True)  #: :meta private:
     async_client: Any = Field(default=None, exclude=True)  #: :meta private: