diff --git a/libs/partners/ai21/langchain_ai21/ai21_base.py b/libs/partners/ai21/langchain_ai21/ai21_base.py
index 0c7c79f64c..5b8fcca0f1 100644
--- a/libs/partners/ai21/langchain_ai21/ai21_base.py
+++ b/libs/partners/ai21/langchain_ai21/ai21_base.py
@@ -16,9 +16,17 @@ class AI21Base(BaseModel):
 
     client: Any = Field(default=None, exclude=True)  #: :meta private:
     api_key: Optional[SecretStr] = None
+    """API key for AI21 API."""
     api_host: Optional[str] = None
+    """Host URL"""
     timeout_sec: Optional[float] = None
+    """Timeout in seconds.
+    
+    If not set, it will default to the value of the environment 
+    variable `AI21_TIMEOUT_SEC` or 300 seconds.
+    """
     num_retries: Optional[int] = None
+    """Maximum number of retries for API requests before giving up."""
 
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
diff --git a/libs/partners/ai21/langchain_ai21/embeddings.py b/libs/partners/ai21/langchain_ai21/embeddings.py
index 87ef389470..9d18c55e4b 100644
--- a/libs/partners/ai21/langchain_ai21/embeddings.py
+++ b/libs/partners/ai21/langchain_ai21/embeddings.py
@@ -15,18 +15,64 @@ def _split_texts_into_batches(texts: List[str], batch_size: int) -> Iterator[Lis
 
 
 class AI21Embeddings(Embeddings, AI21Base):
-    """AI21 embedding model.
+    """AI21 embedding model integration.
 
-    To use, you should have the 'AI21_API_KEY' environment variable set
-    or pass as a named parameter to the constructor.
+        Install ``langchain_ai21`` and set environment variable ``AI21_API_KEY``.
 
-    Example:
+        .. code-block:: bash
+
+            pip install -U langchain_ai21
+            export AI21_API_KEY="your-api-key"
+
+    Key init args — client params:
+      api_key: Optional[SecretStr]
+      batch_size: int
+        The number of texts that will be sent to the API in each batch.
+        Use larger batch sizes if working with many short texts. This will reduce
+        the number of API calls made, and can improve the time it takes to embed
+        a large number of texts.
+      num_retries: Optional[int]
+       Maximum number of retries for API requests before giving up.
+      timeout_sec: Optional[float]
+        Timeout in seconds for API requests. If not set, it will default to the
+        value of the environment variable `AI21_TIMEOUT_SEC` or 300 seconds.
+
+    See full list of supported init args and their descriptions in the params section.
+
+    Instantiate:
         .. code-block:: python
 
             from langchain_ai21 import AI21Embeddings
 
-            embeddings = AI21Embeddings()
-            query_result = embeddings.embed_query("Hello embeddings world!")
+            embed = AI21Embeddings(
+                # api_key="...",
+                # batch_size=128,
+            )
+
+    Embed single text:
+        .. code-block:: python
+
+            input_text = "The meaning of life is 42"
+            vector = embed.embed_query(input_text)
+            print(vector[:3])
+
+        .. code-block:: python
+
+            [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
+
+    Embed multiple texts:
+        .. code-block:: python
+
+             input_texts = ["Document 1...", "Document 2..."]
+            vectors = embed.embed_documents(input_texts)
+            print(len(vectors))
+            # The first 3 coordinates for the first vector
+            print(vectors[0][:3])
+
+        .. code-block:: python
+
+            2
+            [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
     """
 
     batch_size: int = _DEFAULT_BATCH_SIZE