Update factory functions for backwards compatibility.

spacy/cli/templates/quickstart_training.jinja

@@ -238,7 +238,7 @@ grad_factor = 1.0
 {% if "entity_linker" in components -%}
 [components.entity_linker]
 factory = "entity_linker"
-get_candidates = {"@misc":"spacy.CandidateGenerator.v1"}
+get_candidates = {"@misc":"spacy.CandidateGenerator.v2"}
 incl_context = true
 incl_prior = true
 
@@ -517,7 +517,7 @@ width = ${components.tok2vec.model.encode.width}
 {% if "entity_linker" in components -%}
 [components.entity_linker]
 factory = "entity_linker"
-get_candidates = {"@misc":"spacy.CandidateGenerator.v1"}
+get_candidates = {"@misc":"spacy.CandidateGenerator.v2"}
 incl_context = true
 incl_prior = true
 

spacy/ml/models/entity_linker.py

@@ -120,13 +120,28 @@ def empty_kb(
 
 
 @registry.misc("spacy.CandidateGenerator.v1")
-def create_get_candidates() -> Callable[
-    [KnowledgeBase, Iterator[SpanGroup]], Iterator[CandidatesForDocT]
-]:
+def create_get_candidates() -> Callable[[KnowledgeBase, Span], Iterable[Candidate]]:
     return get_candidates
 
 
-def get_candidates(
+@registry.misc("spacy.CandidateGenerator.v2")
+def create_get_candidates_v2() -> Callable[
+    [KnowledgeBase, Iterator[SpanGroup]], Iterator[CandidatesForDocT]
+]:
+    return get_candidates_v2
+
+
+def get_candidates(kb: KnowledgeBase, mention: Span) -> Iterable[Candidate]:
+    """
+    Return candidate entities for the given mention from the KB.
+    kb (KnowledgeBase): Knowledge base to query.
+    mention (Span): Entity mention.
+    RETURNS (Iterable[Candidate]): Identified candidates for specified mention.
+    """
+    return next(next(get_candidates_v2(kb, iter([SpanGroup(mention.doc, spans=[mention])])))[0])
+
+
+def get_candidates_v2(
     kb: KnowledgeBase, mentions: Iterator[SpanGroup]
 ) -> Iterator[Iterable[Iterable[Candidate]]]:
     """

spacy/pipeline/entity_linker.py

@@ -65,7 +65,7 @@ DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"]
         "incl_prior": True,
         "incl_context": True,
         "entity_vector_length": 64,
-        "get_candidates": {"@misc": "spacy.CandidateGenerator.v1"},
+        "get_candidates": {"@misc": "spacy.CandidateGenerator.v2"},
         "overwrite": False,
         "generate_empty_kb": {"@misc": "spacy.EmptyKB.v2"},
         "scorer": {"@scorers": "spacy.entity_linker_scorer.v1"},

spacy/tests/pipeline/test_entity_linker.py

@@ -10,7 +10,7 @@ from spacy.compat import pickle
 from spacy.kb import Candidate, InMemoryLookupKB, KnowledgeBase
 from spacy.lang.en import English
 from spacy.ml import load_kb
-from spacy.ml.models.entity_linker import build_span_maker, get_candidates
+from spacy.ml.models.entity_linker import build_span_maker, get_candidates_v2
 from spacy.pipeline import EntityLinker, TrainablePipe
 from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
 from spacy.scorer import Scorer
@@ -453,16 +453,16 @@ def test_candidate_generation(nlp):
     mykb.add_alias(alias="adam", entities=["Q2"], probabilities=[0.9])
 
     # test the size of the relevant candidates
-    adam_ent_cands = next(get_candidates(mykb, SpanGroup(doc=doc, spans=[adam_ent])))[0]
+    adam_ent_cands = next(get_candidates_v2(mykb, SpanGroup(doc=doc, spans=[adam_ent])))[0]
     assert len(adam_ent_cands) == 1
     assert (
-        len(next(get_candidates(mykb, SpanGroup(doc=doc, spans=[douglas_ent])))[0]) == 2
+        len(next(get_candidates_v2(mykb, SpanGroup(doc=doc, spans=[douglas_ent])))[0]) == 2
     )
     assert (
-        len(next(get_candidates(mykb, SpanGroup(doc=doc, spans=[Adam_ent])))[0]) == 0
+        len(next(get_candidates_v2(mykb, SpanGroup(doc=doc, spans=[Adam_ent])))[0]) == 0
     )  # default case sensitive
     assert (
-        len(next(get_candidates(mykb, SpanGroup(doc=doc, spans=[shrubbery_ent])))[0])
+        len(next(get_candidates_v2(mykb, SpanGroup(doc=doc, spans=[shrubbery_ent])))[0])
         == 0
     )
 

website/docs/api/architectures.mdx

@@ -1255,6 +1255,15 @@ A function that reads an existing `KnowledgeBase` from file.
 | --------- | -------------------------------------------------------- |
 | `kb_path` | The location of the KB that was stored to file. ~~Path~~ |
 
+### spacy.CandidateGenerator.v2 {id="CandidateGenerator-v2"}
+
+A function that takes as input a [`KnowledgeBase`](/api/kb) and a
+`Iterator[SpanGroup]` object denoting a collection of named entities for
+multiple [`Doc`](/api/doc), and returns an iterable of plausible
+[`Candidate`](/api/kb/#candidate) objects per `Doc`. The default
+`CandidateGenerator` uses the text of a mention to find its potential aliases in
+the `KnowledgeBase`. Note that this function is case-dependent.
+
 ### spacy.CandidateGenerator.v1 {id="CandidateGenerator"}
 
 A function that takes as input a [`KnowledgeBase`](/api/kb) and a

website/docs/api/entitylinker.mdx

@@ -47,7 +47,7 @@ architectures and their arguments and hyperparameters.
 >    "incl_context": True,
 >    "model": DEFAULT_NEL_MODEL,
 >    "entity_vector_length": 64,
->    "get_candidates": {'@misc': 'spacy.CandidateGenerator.v1'},
+>    "get_candidates": {'@misc': 'spacy.CandidateGenerator.v2'},
 >    "threshold": None,
 > }
 > nlp.add_pipe("entity_linker", config=config)