update docs

Author: ZiyiXia

.github/workflows/documentation.yml

@@ -13,7 +13,7 @@ jobs:
       - uses: actions/setup-python@v5
       - name: Install dependencies
         run: |
-          pip install . sphinx sphinx_rtd_theme myst_parser myst-nb furo
+          pip install . sphinx myst_parser myst-nb sphinx-design pydata-sphinx-theme
       - name: Sphinx build
         run: |
           sphinx-build docs/source docs/build

README.md

@@ -159,7 +159,7 @@ Currently we are updating the [tutorials](./Tutorials/), we aim to create a comp
 The following contents are releasing in the upcoming weeks:
 
 - Evaluation
-- RAG
+- BGE-EN-ICL
 
 <details>
   <summary>The whole tutorial roadmap</summary>

docs/requirements.txt

@@ -1,3 +1,5 @@
 sphinx
 myst-nb
-furo
+sphinx-design
+pydata-sphinx-theme
+# furo

docs/source/API/abc.rst

@@ -3,4 +3,5 @@ Abstract Class
 
 .. toctree::
     abc/inference
+    abc/evaluation
     abc/finetune

docs/source/API/index.rst

@@ -0,0 +1,11 @@
+API
+===
+
+.. toctree::
+   :hidden:
+   :maxdepth: 1
+
+   abc
+   inference
+   evaluation
+   finetune

docs/source/FAQ/index.rst

@@ -0,0 +1,2 @@
+FAQ
+===

docs/source/Introduction/concept.rst

@@ -0,0 +1,37 @@
+Concept
+=======
+
+Embedder
+--------
+
+Embedder, or embedding model, is a model designed to convert data, usually text, codes, or images, into sparse or dense numerical vectors (embeddings) in a high dimensional vector space.
+These embeddings capture the semantic meaning or key features of the input, which enable efficient comparison and analysis.
+
+A very famous demonstration is the example from `word2vec <https://arxiv.org/abs/1301.3781>`_. It shows how word embeddings capture semantic relationships through vector arithmetic:
+
+.. image:: ../_static/img/word2vec.png
+   :width: 500
+   :align: center
+
+Nowadays, embedders are capable of mapping sentences and even passages into vector space.
+They are widely used in real world tasks such as retrieval, clustering, etc.
+In the era of LLMs, embedding models play a pivot role in RAG, enables LLMs to access and integrate relevant context from vast external datasets.
+
+Reranker
+--------
+
+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.
+Whereas a reranker takes two sentences at the same time and directly computer a score representing their similarity.
+
+The following figure shows their difference:
+
+.. figure:: https://raw.githubusercontent.com/UKPLab/sentence-transformers/master/docs/img/Bi_vs_Cross-Encoder.png
+   :width: 500
+   :align: center
+   
+   Bi-Encoder & Cross-Encoder (from Sentence Transformers)
+
+Although Cross-Encoder usually has better performances than Bi-Encoder, it is extremly time consuming to use Cross-Encoder if we have a great amount of data. 
+Thus a widely accepted approach is to use a Bi-Encoder for initial retrieval (e.g., selecting the top 100 candidates from 100,000 sentences) and then refine the ranking of the selected candidates using a Cross-Encoder for more accurate results.

docs/source/Introduction/index.rst

@@ -0,0 +1,19 @@
+Introduction
+============
+
+BGE builds one-stop retrieval toolkit for search and RAG. We provide inference, evaluation, and fine-tuning for embedding models and reranker.
+
+.. figure:: ../_static/img/RAG_pipeline.png
+   :width: 700
+   :align: center
+
+   BGE embedder and reranker in an RAG pipeline.
+
+Quickly get started with:
+
+.. toctree::
+   :maxdepth: 1
+
+   installation
+   concept
+   quick_start

docs/source/Introduction/installation.rst

@@ -40,4 +40,9 @@ For development in editable mode:
     # If you do not want to finetune the models, you can install the package without the finetune dependency:
     pip install -e .
     # If you want to finetune the models, you can install the package with the finetune dependency:
-    pip install -e .[finetune]
+    pip install -e .[finetune]
+
+PyTorch-CUDA
+------------
+
+If you want to use CUDA GPUs during inference and finetuning, please install appropriate version of `PyTorch <https://pytorch.org/get-started/locally/>`_ with CUDA support.

docs/source/_static/css/custom.css

@@ -0,0 +1,9 @@
+.bd-sidebar-primary {
+    width: 22%;
+    line-height: 1.4;
+}
+
+.col-lg-3 {
+    flex: 0 0 auto;
+    width: 22%;
+}