Repository: deepset-ai/haystack

Issue:
Doc: API Reference argument options underindented
Minor issue: the options for arguments (`model_format` and `pooling_strategy`) in the API Reference [EmbeddingRetriever](https://docs.haystack.deepset.ai/reference/retriever-api#embeddingretriever) is under-indented. Might also exist in other parts of the doc.

![Doc_Alignment](https://user-images.githubusercontent.com/4636315/236397043-b8e0c65e-331d-4374-b678-111b63aef388.png)

Might need some modification in the reference generation process.


Discussion:
Hi, I could take this one :)
Hi @pandasar13, you can have this one! Dont forget to register from [Hacktoberfest](https://hacktoberfest.com/) website if you want to participate in Hacktoberfest. When you open a PR, I'll send you a link to a form to collect your information :) 
Hi @bilgeyucel the assignee seems unresponsive, if you allow can i work on this one?
Hi! Sorry! I was still planning on doing it by the end of the week (sorry about not replying). But if you want to work on it @SoNiC-HeRE , no problem from my side :)
> Hi! Sorry! I was still planning on doing it by the end of the week (sorry about not replying). But if you want to work on it @SoNiC-HeRE , no problem from my side :)

nope, it's fine i thought of giving it a try but if you have it scheduled i can look for other issue to help with ;)
Hey Can I be assigned if its still open?🙋‍♂️ I can make the PR for the same rightaway!


Resolving patch:
diff --git a/haystack/nodes/retriever/dense.py b/haystack/nodes/retriever/dense.py
--- a/haystack/nodes/retriever/dense.py
+++ b/haystack/nodes/retriever/dense.py
@@ -1481,19 +1481,21 @@ def __init__(
                              provided, it will be inferred automatically from the model configuration files.
                              Options:
 
-                             - ``'farm'`` (will use `_DefaultEmbeddingEncoder` as embedding encoder)
-                             - ``'transformers'`` (will use `_DefaultEmbeddingEncoder` as embedding encoder)
-                             - ``'sentence_transformers'`` (will use `_SentenceTransformersEmbeddingEncoder` as embedding encoder)
-                             - ``'retribert'`` (will use `_RetribertEmbeddingEncoder` as embedding encoder)
-                             - ``'openai'``: (will use `_OpenAIEmbeddingEncoder` as embedding encoder)
-                             - ``'cohere'``: (will use `_CohereEmbeddingEncoder` as embedding encoder)
+        1. `farm` : (will use `_DefaultEmbeddingEncoder` as embedding encoder)
+        2. `transformers` : (will use `_DefaultEmbeddingEncoder` as embedding encoder)
+        3. `sentence_transformers` : (will use `_SentenceTransformersEmbeddingEncoder` as embedding encoder)
+        4. `retribert` : (will use `_RetribertEmbeddingEncoder` as embedding encoder)
+        5. `openai` : (will use `_OpenAIEmbeddingEncoder` as embedding encoder)
+        6. `cohere` : (will use `_CohereEmbeddingEncoder` as embedding encoder)
+
         :param pooling_strategy: Strategy for combining the embeddings from the model (for farm / transformers models only).
                                  Options:
 
-                                 - ``'cls_token'`` (sentence vector)
-                                 - ``'reduce_mean'`` (sentence vector)
-                                 - ``'reduce_max'`` (sentence vector)
-                                 - ``'per_token'`` (individual token vectors)
+        1. `cls_token` (sentence vector)
+        2. `reduce_mean` (sentence vector)
+        3. `reduce_max` (sentence vector)
+        4. `per_token` (individual token vectors)
+
         :param emb_extraction_layer: Number of layer from which the embeddings shall be extracted (for farm / transformers models only).
                                      Default: -1 (very last layer).
         :param top_k: How many documents to return per query.


Test patch: