community[patch]: Adopting the lighter-weight xinference_client (#21900)

While integrating the xinference_embedding, we observed that the
downloaded dependency package is quite substantial in size. With a focus
on resource optimization and efficiency, if the project requirements are
limited to its vector processing capabilities, we recommend migrating to
the xinference_client package. This package is more streamlined,
significantly reducing the storage space requirements of the project and
maintaining a feature focus, making it particularly suitable for
scenarios that demand lightweight integration. Such an approach not only
boosts deployment efficiency but also enhances the application's
maintainability, rendering it an optimal choice for our current context.

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>

libs/community/tests/integration_tests/embeddings/test_xinference.py

@@ -73,3 +73,13 @@ def test_xinference_embedding_query(setup: Tuple[str, str]) -> None:
     document = "foo bar"
     output = xinference.embed_query(document)
     assert len(output) == 4096
+
+
+def test_xinference_embedding() -> None:
+    embedding_model = XinferenceEmbeddings(
+        server_url="http://xinference-hostname:9997", model_uid="foo"
+    )
+
+    embedding_model.embed_documents(
+        texts=["hello", "i'm trying to upgrade xinference embedding"]
+    )