multimodal doc

docs/multimodal_models.rst

@@ -6,3 +6,48 @@ image captioning, visual questions, and more. The ImageArray class enables handl
 image data within a pandas DataFrame. Currently supports these image formats:
 PIL images, numpy arrays, base64 strings, and image URLs
 
+Initializing ImageArray
+-----------------------
+The ImageArray class is an extension array designed to handle images as data types in pandas. 
+You can initilize an ImageArray with a list of supported image formats
+
+.. code-block:: python
+
+    from PIL import Image
+    import numpy as np
+    from lotus.utils import ImageArray
+
+    # Example image inputs
+    image1 = Image.open("path_to_image1.jpg")
+    image2 = np.random.randint(0, 255, (100, 100, 3), dtype="uint8")
+
+    # Create an ImageArray
+    images = ImageArray([image1, image2, None])
+
+
+Loading ImageArray
+-------------------
+
+The ImageArray supports multiple input formats for loading images. 
+- **PIL Images** : Directly pass a PIL image object
+- **Numpy Arrays** : Convert numpy arrays to PIL Images automatically
+- **Base64 Strings** : Decode base 64 strings into images
+- **URLs** :Fetch images from HTTP/HTTPS URLs
+- **File Paths** : Load images from local or remote file Paths
+- **S3 URLs** : Fetch images stored in S3 buckets
+
+Example:
+
+.. code-block:: python
+
+    from lotus.utils import fetch_image
+    from PIL import Image
+
+    image_path = "path_to_image.jpg"
+    image_url = "https://example.com/image.png"
+    base64_image = "data:image/png;base64,..." 
+
+    # Load images
+    pil_image = fetch_image(image_path)
+    url_image = fetch_image(image_url)
+    base64_image_obj = fetch_image(base64_image)