From 332ffed393ef7e789e991febfc2672e574780834 Mon Sep 17 00:00:00 2001
From: Liuww <49828783+liuwwang@users.noreply.github.com>
Date: Tue, 21 May 2024 06:05:09 +0800
Subject: [PATCH] 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>
---
 .../embeddings/xinference.py                  | 27 ++++++++++++++-----
 .../embeddings/test_xinference.py             | 10 +++++++
 2 files changed, 31 insertions(+), 6 deletions(-)

diff --git a/libs/community/langchain_community/embeddings/xinference.py b/libs/community/langchain_community/embeddings/xinference.py
index db9b56fcbf9..858a2fea415 100644
--- a/libs/community/langchain_community/embeddings/xinference.py
+++ b/libs/community/langchain_community/embeddings/xinference.py
@@ -1,11 +1,11 @@
 """Wrapper around Xinference embedding models."""
+
 from typing import Any, List, Optional
 
 from langchain_core.embeddings import Embeddings
 
 
 class XinferenceEmbeddings(Embeddings):
-
     """Xinference embedding models.
 
     To use, you should have the xinference library installed:
@@ -14,6 +14,12 @@ class XinferenceEmbeddings(Embeddings):
 
         pip install xinference
 
+    If you're simply using the services provided by Xinference, you can utilize the xinference_client package:
+
+    .. code-block:: bash
+
+        pip install xinference_client
+
     Check out: https://github.com/xorbitsai/inference
     To run, you need to start a Xinference supervisor on one server and Xinference workers on the other servers.
 
@@ -32,6 +38,12 @@ class XinferenceEmbeddings(Embeddings):
 
            $ xinference-supervisor
 
+        If you're simply using the services provided by Xinference, you can utilize the xinference_client package:
+
+        .. code-block:: bash
+
+            pip install xinference_client
+
         Starting the worker:
 
         .. code-block:: bash
@@ -72,11 +84,14 @@ class XinferenceEmbeddings(Embeddings):
     ):
         try:
             from xinference.client import RESTfulClient
-        except ImportError as e:
-            raise ImportError(
-                "Could not import RESTfulClient from xinference. Please install it"
-                " with `pip install xinference`."
-            ) from e
+        except ImportError:
+            try:
+                from xinference_client import RESTfulClient
+            except ImportError as e:
+                raise ImportError(
+                    "Could not import RESTfulClient from xinference. Please install it"
+                    " with `pip install xinference` or `pip install xinference_client`."
+                ) from e
 
         super().__init__()
 
diff --git a/libs/community/tests/integration_tests/embeddings/test_xinference.py b/libs/community/tests/integration_tests/embeddings/test_xinference.py
index 6c52fa98981..13d441ed133 100644
--- a/libs/community/tests/integration_tests/embeddings/test_xinference.py
+++ b/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"]
+    )