diff --git a/libs/partners/openai/langchain_openai/embeddings/base.py b/libs/partners/openai/langchain_openai/embeddings/base.py
index 8894364b24fcb..28a1e4137bc15 100644
--- a/libs/partners/openai/langchain_openai/embeddings/base.py
+++ b/libs/partners/openai/langchain_openai/embeddings/base.py
@@ -99,23 +99,90 @@ def _process_batched_chunked_embeddings(
 
 
 class OpenAIEmbeddings(BaseModel, Embeddings):
-    """OpenAI embedding models.
+    """OpenAI embedding model integration.
 
-    To use, you should have the
-    environment variable ``OPENAI_API_KEY`` set with your API key or pass it
-    as a named parameter to the constructor.
+    Setup:
+        Install ``langchain_openai`` and set environment variable ``OPENAI_API_KEY``.
 
-    In order to use the library with Microsoft Azure endpoints, use
-    AzureOpenAIEmbeddings.
+        .. code-block:: bash
 
-    Example:
+            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
 
-            model = OpenAIEmbeddings(model="text-embedding-3-large")
+            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
+            )
+
+    Embed multiple texts:
+
+        .. 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]
+
+    Embed single text:
+
+        .. code-block:: python
+
+            input_text = "The meaning of life is 42"
+            vector = embeddings.embed_query('hello')
+            print(vector[:3])
+
+        .. code-block:: python
+
+            [-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:
     model: str = "text-embedding-ada-002"