community[patch]: ElasticsearchStore: add relevance function selector (#16378)

Implement similarity function selector for ElasticsearchStore. The
scores coming back from Elasticsearch are already similarities (not
distances) and they are already normalized (see
[docs](https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html#dense-vector-params)).
Hence we leave the scores untouched and just forward them.

This fixes #11539.

However, in hybrid mode (when keyword search and vector search are
involved) Elasticsearch currently returns no scores. This PR adds an
error message around this fact. We need to think a bit more to come up
with a solution for this case.

This PR also corrects a small error in the Elasticsearch integration
test.

---------

Co-authored-by: Erick Friis <erick@langchain.dev>

libs/community/tests/unit_tests/vectorstores/test_elasticsearch.py

@@ -0,0 +1,32 @@
+"""Test Elasticsearch functionality."""
+import pytest
+
+from langchain_community.vectorstores.elasticsearch import (
+    ApproxRetrievalStrategy,
+    ElasticsearchStore,
+)
+from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
+
+
+@pytest.mark.requires("elasticsearch")
+def test_elasticsearch_hybrid_scores_guard() -> None:
+    """Ensure an error is raised when search with score in hybrid mode
+    because in this case Elasticsearch does not return any score.
+    """
+    from elasticsearch import Elasticsearch
+
+    query_string = "foo"
+    embeddings = FakeEmbeddings()
+
+    store = ElasticsearchStore(
+        index_name="dummy_index",
+        es_connection=Elasticsearch(hosts=["http://dummy-host:9200"]),
+        embedding=embeddings,
+        strategy=ApproxRetrievalStrategy(hybrid=True),
+    )
+    with pytest.raises(ValueError):
+        store.similarity_search_with_score(query_string)
+
+    embedded_query = embeddings.embed_query(query_string)
+    with pytest.raises(ValueError):
+        store.similarity_search_by_vector_with_relevance_scores(embedded_query)