update docs

docs/requirements.txt

@@ -1,5 +1,6 @@
 sphinx
 myst-nb
+myst_parser
 sphinx-design
 pydata-sphinx-theme
 # furo

docs/source/Introduction/reranker.rst

@@ -1,6 +1,10 @@
 Reranker
 ========
 
+.. tip::
+   
+   If you are already familiar with the concepts, take a look at the :doc:`BGE rerankers <../bge/index>`!
+
 Reranker, or Cross-Encoder, is a model that refines the ranking of candidate pairs (e.g., query-document pairs) by jointly encoding and scoring them.
 
 Typically, we use embedder as a Bi-Encoder. It first computes the embeddings of two input sentences, then compute their similarity using metrics such as cosine similarity or Euclidean distance.

docs/source/bge/bge_m3.rst

@@ -3,6 +3,7 @@ BGE-M3
 ======
 
 BGE-M3 is a compound and powerful embedding model distinguished for its versatility in:
+
 - **Multi-Functionality**: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval.
 - **Multi-Linguality**: It can support more than 100 working languages.
 - **Multi-Granularity**: It is able to process inputs of different granularities, spanning from short sentences to long documents of up to 8192 tokens.