From 1374b98da4ddc9d9155752684ef762b1e4da3e1a Mon Sep 17 00:00:00 2001 From: ZiyiXia Date: Tue, 3 Dec 2024 11:31:38 +0000 Subject: [PATCH] update docs --- .github/workflows/documentation.yml | 2 +- README.md | 2 +- docs/requirements.txt | 4 +- docs/source/API/abc.rst | 1 + docs/source/API/index.rst | 11 ++ docs/source/FAQ/index.rst | 2 + docs/source/Introduction/concept.rst | 37 +++++ docs/source/Introduction/index.rst | 19 +++ docs/source/Introduction/installation.rst | 7 +- docs/source/_static/css/custom.css | 9 ++ docs/source/_static/img/RAG_pipeline.png | Bin 0 -> 303958 bytes docs/source/_static/img/word2vec.png | Bin 0 -> 69317 bytes docs/source/bge/bge_m3.rst | 117 ++++++++++++++- .../bge/{bge_v1.rst => bge_v1_v1.5.rst} | 38 ++++- docs/source/bge/index.rst | 19 +++ docs/source/bge/introduction.rst | 5 - docs/source/community/index.rst | 2 + docs/source/conf.py | 59 ++++++-- docs/source/index.rst | 139 ++++++++++++++---- docs/source/tutorial/index.rst | 14 ++ 20 files changed, 435 insertions(+), 52 deletions(-) create mode 100644 docs/source/API/index.rst create mode 100644 docs/source/FAQ/index.rst create mode 100644 docs/source/Introduction/concept.rst create mode 100644 docs/source/Introduction/index.rst create mode 100644 docs/source/_static/css/custom.css create mode 100644 docs/source/_static/img/RAG_pipeline.png create mode 100644 docs/source/_static/img/word2vec.png rename docs/source/bge/{bge_v1.rst => bge_v1_v1.5.rst} (83%) create mode 100644 docs/source/bge/index.rst delete mode 100644 docs/source/bge/introduction.rst create mode 100644 docs/source/community/index.rst create mode 100644 docs/source/tutorial/index.rst diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml index ce168cb7..3a33b38d 100644 --- a/.github/workflows/documentation.yml +++ b/.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 diff --git a/README.md b/README.md index d85f391c..bfcd26d0 100644 --- a/README.md +++ b/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
The whole tutorial roadmap diff --git a/docs/requirements.txt b/docs/requirements.txt index 3d310f10..7ac4bb04 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,3 +1,5 @@ sphinx myst-nb -furo \ No newline at end of file +sphinx-design +pydata-sphinx-theme +# furo \ No newline at end of file diff --git a/docs/source/API/abc.rst b/docs/source/API/abc.rst index 4da03d98..c00b4220 100644 --- a/docs/source/API/abc.rst +++ b/docs/source/API/abc.rst @@ -3,4 +3,5 @@ Abstract Class .. toctree:: abc/inference + abc/evaluation abc/finetune \ No newline at end of file diff --git a/docs/source/API/index.rst b/docs/source/API/index.rst new file mode 100644 index 00000000..65056189 --- /dev/null +++ b/docs/source/API/index.rst @@ -0,0 +1,11 @@ +API +=== + +.. toctree:: + :hidden: + :maxdepth: 1 + + abc + inference + evaluation + finetune \ No newline at end of file diff --git a/docs/source/FAQ/index.rst b/docs/source/FAQ/index.rst new file mode 100644 index 00000000..4515e7cf --- /dev/null +++ b/docs/source/FAQ/index.rst @@ -0,0 +1,2 @@ +FAQ +=== \ No newline at end of file diff --git a/docs/source/Introduction/concept.rst b/docs/source/Introduction/concept.rst new file mode 100644 index 00000000..616a1e14 --- /dev/null +++ b/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 `_. 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. diff --git a/docs/source/Introduction/index.rst b/docs/source/Introduction/index.rst new file mode 100644 index 00000000..72fca534 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/docs/source/Introduction/installation.rst b/docs/source/Introduction/installation.rst index 4aacbb27..d54739b9 100644 --- a/docs/source/Introduction/installation.rst +++ b/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] \ No newline at end of file + pip install -e .[finetune] + +PyTorch-CUDA +------------ + +If you want to use CUDA GPUs during 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Multi-Functionality, Multi-Linguality, and Multi-Granularity | ++-------------------------------------------------------------------+-----------------+------------+--------------+-----------------------------------------------------------------------+ + +Multi-Linguality +================ + +BGE-M3 was trained on multiple datasets covering up to 170+ different languages. +While the amount of training data on languages are highly unbalanced, the actual model performance on different languages will have difference. + +For more information of datasets and evaluation results, please check out our `paper `_ for details. + +Multi-Granularity +================= + +We extend the max position to 8192, enabling the embedding of larger corpus. +Proposing a simple but effective method: MCLS (Multiple CLS) to enhance the model's ability on long text without additional fine-tuning. + +Multi-Functionality +=================== + +.. code:: python + + from FlagEmbedding import BGEM3FlagModel + + model = BGEM3FlagModel('BAAI/bge-m3') + sentences_1 = ["What is BGE M3?", "Defination of BM25"] + sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.", + "BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"] + +Dense Retrieval +--------------- + +Similar to BGE v1 or v1.5 models, BGE-M3 use the normalized hidden state of the special token [CLS] as the dense embedding: + +.. math:: e_q = norm(H_q[0]) + +Next, to compute the relevance score between the query and passage: + +.. math:: s_{dense}=f_{sim}(e_p, e_q) + +where :math:`e_p, e_q` are the embedding vectors of passage and query, respectively. + +:math:`f_{sim}` is the score function (such as inner product and L2 distance) for comupting two embeddings' similarity. + +Sparse Retrieval +---------------- + +BGE-M3 generates sparce embeddings by adding a linear layer and a ReLU activation function following the hidden states: + +.. math:: w_{qt} = \text{Relu}(W_{lex}^T H_q [i]) + +where :math:`W_{lex}` representes the weights of linear layer and :math:`H_q[i]` is the encoder's output of the :math:`i^{th}` token. + +Based on the tokens' weights of query and passage, the relevance score between them is computed by the joint importance of the co-existed terms within the query and passage: + +.. math:: s_{lex} = \sum_{t\in q\cap p}(w_{qt} * w_{pt}) + +where :math:`w_{qt}, w_{pt}` are the importance weights of each co-existed term :math:`t` in query and passage, respectively. + +Multi-Vector +------------ + +The multi-vector method utilizes the entire output embeddings for the representation of query :math:`E_q` and passage :math:`E_p`. + +.. math:: + + E_q = norm(W_{mul}^T H_q) + + E_p = norm(W_{mul}^T H_p) + +where :math:`W_{mul}` is the learnable projection matrix. + +Following ColBert, BGE-M3 use late-interaction to compute the fine-grained relevance score: + +.. math:: s_{mul}=\frac{1}{N}\sum_{i=1}^N\max_{j=1}^M E_q[i]\cdot E_p^T[j] + +where :math:`E_q, E_p` are the entire output embeddings of query and passage, respectively. + +This is a summation of average of maximum similarity of each :math:`v\in E_q` with vectors in :math:`E_p`. + +Hybrid Ranking +-------------- + +BGE-M3's multi-functionality gives the possibility of hybrid ranking to improve retrieval. +Firstly, due to the heavy cost of multi-vector method, we can retrieve the candidate results by either of the dense or sparse method. +Then, to get the final result, we can rerank the candidates based on the integrated relevance score: + +.. math:: s_{rank} = w_1\cdot s_{dense}+w_2\cdot s_{lex} + w_3\cdot s_{mul} + +where the values chosen for :math:`w_1`, :math:`w_2` and :math:`w_3` varies depending on the downstream scenario. + + +Usage +===== + +.. code:: python + + from FlagEmbedding import BGEM3FlagModel + + model = BGEM3FlagModel('BAAI/bge-m3') + + sentences_1 = ["What is BGE M3?", "Defination of BM25"] + + output = model.encode(sentences_1, return_dense=True, return_sparse=True, return_colbert_vecs=True) + dense, sparse, multiv = output['dense_vecs'], output['lexical_weights'], output['colbert_vecs'] \ No newline at end of file diff --git a/docs/source/bge/bge_v1.rst b/docs/source/bge/bge_v1_v1.5.rst similarity index 83% rename from docs/source/bge/bge_v1.rst rename to docs/source/bge/bge_v1_v1.5.rst index 6c141ca4..8e5d3911 100644 --- a/docs/source/bge/bge_v1.rst +++ b/docs/source/bge/bge_v1_v1.5.rst @@ -1,5 +1,7 @@ -BGE-v1 -====== +BGE v1 & v1.5 +============= + +BGE v1 and v1.5 are series of encoder only models base on BERT. They achieved best performance among the models of the same size at the time of release. BGE --- @@ -26,7 +28,7 @@ C-MTEB benchmarks at the time released. BGE-v1.5 -------- -Then to enhance its retrieval ability without instruction and alleviate the issue of the similarity distribution, :code:`bge-*-1.5` models +Then to enhance its retrieval ability without instruction and alleviate the issue of the similarity distribution, :code:`bge-*-v1.5` models were released in Sep 2023. They are still the most popular embedding models that balanced well between embedding quality and model sizes. +-----------------------------------------------------------------------------+-----------+------------+--------------+--------------+ @@ -37,8 +39,8 @@ were released in Sep 2023. They are still the most popular embedding models that | `BAAI/bge-base-en-v1.5 `_ | English | 109M | 438 MB | reasonable | +-----------------------------------------------------------------------------+-----------+------------+--------------+ similarity + | `BAAI/bge-small-en-v1.5 `_ | English | 33.4M | 133 MB | distribution | -+-----------------------------------------------------------------------------+-----------+------------+--------------+ + -| `BAAI/bge-large-zh-v1.5 `_ | Chinese | 326M | 1.3 GB | | ++-----------------------------------------------------------------------------+-----------+------------+--------------+ and better + +| `BAAI/bge-large-zh-v1.5 `_ | Chinese | 326M | 1.3 GB | performance | +-----------------------------------------------------------------------------+-----------+------------+--------------+ + | `BAAI/bge-base-zh-v1.5 `_ | Chinese | 102M | 409 MB | | +-----------------------------------------------------------------------------+-----------+------------+--------------+ + @@ -46,4 +48,30 @@ were released in Sep 2023. They are still the most popular embedding models that +-----------------------------------------------------------------------------+-----------+------------+--------------+--------------+ +Usage +----- + +To use BGE v1 or v1.5 model for inference, load model through `` + +.. code:: python + + from FlagEmbedding import FlagModel + + model = FlagModel('BAAI/bge-base-en-v1.5') + + sentences = ["Hello world", "I am inevitable"] + embeddings = model.encode(sentences) + +.. tip:: + + For simple tasks that only encode a few sentences like above, it's faster to use single GPU comparing to multi-GPUs: + + .. code:: python + + import os + os.environ['CUDA_VISIBLE_DEVICES'] = '0' + or + + .. code:: python + model = FlagModel('BAAI/bge-base-en-v1.5', devices=0) \ No newline at end of file diff --git a/docs/source/bge/index.rst b/docs/source/bge/index.rst new file mode 100644 index 00000000..9c400806 --- /dev/null +++ b/docs/source/bge/index.rst @@ -0,0 +1,19 @@ +BGE +=== + +**BGE** stands for **BAAI General Embeddings**, which is a series of embedding models released by BAAI. + +.. toctree:: + :maxdepth: 1 + :caption: Embedder + + bge_v1_v1.5 + bge_m3 + bge_icl + +.. toctree:: + :maxdepth: 1 + :caption: Embedder + + bge_reranker + diff --git a/docs/source/bge/introduction.rst b/docs/source/bge/introduction.rst deleted file mode 100644 index 86cb3ce7..00000000 --- a/docs/source/bge/introduction.rst +++ /dev/null @@ -1,5 +0,0 @@ -Introduction -============ - -**BGE** stands for **BAAI General Embeddings**, which is a series of embedding models released by BAAI. - diff --git a/docs/source/community/index.rst b/docs/source/community/index.rst new file mode 100644 index 00000000..c83c197b --- /dev/null +++ b/docs/source/community/index.rst @@ -0,0 +1,2 @@ +Community +========= \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index c1220b71..48b5d092 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -24,6 +24,7 @@ "sphinx.ext.githubpages", "sphinx.ext.viewcode", "sphinx.ext.coverage", + "sphinx_design", "myst_nb", ] @@ -33,21 +34,55 @@ # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output -html_theme = 'furo' -# html_logo = "_static/img/BAAI_logo.png" +# html_theme = 'furo' +html_theme = "pydata_sphinx_theme" +html_logo = "_static/img/BAAI_logo.png" html_title = "FlagEmbedding" html_static_path = ['_static'] +html_css_files = ["css/custom.css"] html_theme_options = { - # "light_logo": "/_static/img/BAAI_logo.png", - "light_css_variables": { - "color-brand-primary": "#238be8", - "color-brand-content": "#238be8", - }, - "dark_css_variables": { - "color-brand-primary": "#FBCB67", - "color-brand-content": "#FBCB67", - }, +# # "light_logo": "/_static/img/BAAI_logo.png", +# "light_css_variables": { +# "color-brand-primary": "#238be8", +# "color-brand-content": "#238be8", +# }, +# "dark_css_variables": { +# "color-brand-primary": "#FBCB67", +# "color-brand-content": "#FBCB67", +# }, + "navigation_depth": 5, } # MyST-NB conf -nb_execution_mode = "off" \ No newline at end of file +nb_execution_mode = "off" + +html_theme_options = { + "external_links": [ + { + "url": "https://huggingface.co/collections/BAAI/bge-66797a74476eb1f085c7446d", + "name": "HF Models", + }, + ], + "icon_links":[ + { + "name": "GitHub", + "url": "https://github.com/FlagOpen/FlagEmbedding", + "icon": "fa-brands fa-github", + }, + { + "name": "PyPI", + "url": "https://pypi.org/project/FlagEmbedding/", + "icon": "fa-brands fa-python", + }, + { + "name": "HF Models", + "url": "https://huggingface.co/collections/BAAI/bge-66797a74476eb1f085c7446d", + "icon": "fa-solid fa-cube", + } + ], + "header_links_before_dropdown": 7, +} + +html_context = { + "default_mode": "light" +} \ No newline at end of file diff --git a/docs/source/index.rst b/docs/source/index.rst index 6c4ba5e7..702388a7 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -3,23 +3,100 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -BAAI General Embedding -====================== +:html_theme.sidebar_secondary.remove: True -| -| -.. image:: _static/img/bge_logo.jpg - :target: https://github.com/FlagOpen/FlagEmbedding - :width: 500 +BGE +=== + +Welcome to BGE documentation! + +.. figure:: _static/img/bge_logo.jpg + :width: 400 :align: center -| -| +.. grid:: 3 + :gutter: 3 -Welcome to BGE documentation! + .. grid-item-card:: :octicon:`milestone` Introduction + + New to BGE? Quickly get hands-on information. + + +++ + + .. button-ref:: Introduction/index + :expand: + :color: primary + :click-parent: + + To Introduction + + + .. grid-item-card:: :octicon:`package` BGE Models + + Get to know BGE embedding models and rerankers. + + +++ + + .. button-ref:: bge/index + :expand: + :color: primary + :click-parent: + + To BGE + + + .. grid-item-card:: :octicon:`log` Tutorials + + Find useful tutorials to start with if you are looking for guidance + + +++ + + .. button-ref:: tutorial/index + :expand: + :color: primary + :click-parent: + + To Tutorials + + .. grid-item-card:: :octicon:`codescan` API + + Check the API of classes and functions in FlagEmbedding. + + +++ + + .. button-ref:: API/index + :expand: + :color: primary + :click-parent: -We aim for building one-stop retrieval toolkit for search and RAG. + To APIs + + .. grid-item-card:: :octicon:`question` FAQ + + Take a look of questions people frequently asked. + + +++ + + .. button-ref:: FAQ/index + :expand: + :color: primary + :click-parent: + + To FAQ + + .. grid-item-card:: :octicon:`people` Community + + Welcome to join BGE community! + + +++ + + .. button-ref:: community/index + :expand: + :color: primary + :click-parent: + + To Community Besides the resources we provide here in this documentation, please visit our `GitHub repo `_ for more contents including: @@ -49,27 +126,39 @@ BGE is developed by Beijing Academy of Artificial Intelligence (BAAI). :maxdepth: 1 :caption: Introduction - Introduction/installation - Introduction/quick_start + Introduction/index .. toctree:: :hidden: - :maxdepth: 5 - :caption: API + :maxdepth: 1 + :caption: BGE - API/abc - API/inference - API/evaluation - API/finetune + bge/index .. toctree:: :hidden: :maxdepth: 2 :caption: Tutorials - tutorial/1_Embedding - tutorial/2_Metrics - tutorial/3_Indexing - tutorial/4_Evaluation - tutorial/5_Reranking - tutorial/6_RAG \ No newline at end of file + tutorial/index + +.. toctree:: + :hidden: + :maxdepth: 5 + :caption: API + + API/index + +.. toctree:: + :hidden: + :maxdepth: 1 + :caption: FAQ + + FAQ/index + +.. toctree:: + :hidden: + :maxdepth: 1 + :caption: Community + + community/index \ No newline at end of file diff --git a/docs/source/tutorial/index.rst b/docs/source/tutorial/index.rst new file mode 100644 index 00000000..c7b566fc --- /dev/null +++ b/docs/source/tutorial/index.rst @@ -0,0 +1,14 @@ +Tutorials +========= + +.. toctree:: + :hidden: + :maxdepth: 1 + :caption: Tutorials + + 1_Embedding + 2_Metrics + 3_Indexing + 4_Evaluation + 5_Reranking + 6_RAG \ No newline at end of file