community[minor]: Add keybert-based link extractor (#24311)

- **Description:** Add a `KeybertLinkExtractor` for graph vectorstores.
This allows extracting links from keywords in a Document and linking
nodes that have common keywords.
- **Issue:** None
- **Dependencies:** None.

---------

Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: ccurme <chester.curme@gmail.com>

libs/community/tests/integration_tests/graph_vectorstores/extractors/test_keybert_link_extractor.py

@@ -0,0 +1,64 @@
+import pytest
+from langchain_core.graph_vectorstores.links import Link
+
+from langchain_community.graph_vectorstores.extractors import KeybertLinkExtractor
+
+PAGE_1 = """
+Supervised learning is the machine learning task of learning a function that
+maps an input to an output based on example input-output pairs. It infers a
+function from labeled training data consisting of a set of training examples. In
+supervised learning, each example is a pair consisting of an input object
+(typically a vector) and a desired output value (also called the supervisory
+signal). A supervised learning algorithm analyzes the training data and produces
+an inferred function, which can be used for mapping new examples. An optimal
+scenario will allow for the algorithm to correctly determine the class labels
+for unseen instances. This requires the learning algorithm to generalize from
+the training data to unseen situations in a 'reasonable' way (see inductive
+bias).
+"""
+
+PAGE_2 = """
+KeyBERT is a minimal and easy-to-use keyword extraction technique that leverages
+BERT embeddings to create keywords and keyphrases that are most similar to a
+document.
+"""
+
+
+@pytest.mark.requires("keybert")
+def test_one_from_keywords() -> None:
+    extractor = KeybertLinkExtractor()
+
+    results = extractor.extract_one(PAGE_1)
+    assert results == {
+        Link.bidir(kind="kw", tag="supervised"),
+        Link.bidir(kind="kw", tag="labels"),
+        Link.bidir(kind="kw", tag="labeled"),
+        Link.bidir(kind="kw", tag="learning"),
+        Link.bidir(kind="kw", tag="training"),
+    }
+
+
+@pytest.mark.requires("keybert")
+def test_many_from_keyphrases() -> None:
+    extractor = KeybertLinkExtractor(
+        extract_keywords_kwargs={
+            "keyphrase_ngram_range": (1, 2),
+        }
+    )
+
+    results = list(extractor.extract_many([PAGE_1, PAGE_2]))
+    assert results[0] == {
+        Link.bidir(kind="kw", tag="supervised"),
+        Link.bidir(kind="kw", tag="labeled training"),
+        Link.bidir(kind="kw", tag="supervised learning"),
+        Link.bidir(kind="kw", tag="examples supervised"),
+        Link.bidir(kind="kw", tag="signal supervised"),
+    }
+
+    assert results[1] == {
+        Link.bidir(kind="kw", tag="keyphrases"),
+        Link.bidir(kind="kw", tag="keyword extraction"),
+        Link.bidir(kind="kw", tag="keybert"),
+        Link.bidir(kind="kw", tag="keywords keyphrases"),
+        Link.bidir(kind="kw", tag="keybert minimal"),
+    }