docs: improve some multimodal docstrings (#342)

Author: anakin87

haystack_experimental/components/embedders/image/sentence_transformers_doc_image_embedder.py

@@ -87,9 +87,16 @@ def __init__(
         :param root_path: The root directory path where document files are located. If provided, file paths in
             document metadata will be resolved relative to this path. If None, file paths are treated as absolute paths.
         :param model:
-            The Sentence Transformers model to use for calculating embeddings. To be used with this component,
-            the model must be able to embed images and text into the same vector space.
-            Pass a local path or ID of the model on Hugging Face.
+            The Sentence Transformers model to use for calculating embeddings. Pass a local path or ID of the model on
+            Hugging Face. To be used with this component, the model must be able to embed images and text into the same
+            vector space. Compatible models include:
+            - "sentence-transformers/clip-ViT-B-32"
+            - "sentence-transformers/clip-ViT-L-14"
+            - "sentence-transformers/clip-ViT-B-16"
+            - "sentence-transformers/clip-ViT-B-32-multilingual-v1"
+            - "jinaai/jina-embeddings-v4"
+            - "jinaai/jina-clip-v1"
+            - "jinaai/jina-clip-v2".
         :param device:
             The device to use for loading the model.
             Overrides the default device.

haystack_experimental/components/extractors/llm_document_content_extractor.py

@@ -100,7 +100,8 @@ def __init__(
         Initialize the LLMDocumentContentExtractor component.
 
         :param chat_generator: A ChatGenerator instance representing the LLM used to extract text. This generator must
-            support vision-based input and return a plain text response.
+            support vision-based input and return a plain text response. Currently, the experimental versions of
+            OpenAIChatGenerator and AmazonBedrockChatGenerator are supported.
         :param prompt: Instructional text provided to the LLM. It must not contain Jinja variables.
             The prompt should only contain instructions on how to extract the content of the image-based document.
         :param file_path_meta_field: The metadata field in the Document that contains the file path to the image or PDF.