chore: import upstream snapshot with attribution

docs/source/Introduction/quick_start.rst

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+Quick Start
+===========
+
+First, load one of the BGE embedding model:
+
+.. code:: python
+
+    from FlagEmbedding import FlagAutoModel
+
+    model = FlagAutoModel.from_finetuned('BAAI/bge-base-en-v1.5')
+
+.. tip::
+
+    If there's difficulty connecting to Hugging Face, you can use the `HF mirror <https://hf-mirror.com/>`_ instead.
+
+    .. code:: bash
+
+        export HF_ENDPOINT=https://hf-mirror.com
+
+Then, feed some sentences to the model and get their embeddings:
+
+.. code:: python
+
+    sentences_1 = ["I love NLP", "I love machine learning"]
+    sentences_2 = ["I love BGE", "I love text retrieval"]
+    embeddings_1 = model.encode(sentences_1)
+    embeddings_2 = model.encode(sentences_2)
+
+Once we get the embeddings, we can compute similarity by inner product:
+
+.. code:: python
+
+    similarity = embeddings_1 @ embeddings_2.T
+    print(similarity)