community[patch]: update the default hf bge embeddings (#22627)

**Description:** This updates the langchain_community > huggingface > default bge embeddings ([the current default recommends this change](https://huggingface.co/BAAI/bge-large-en)) **Issue:** None **Dependencies:** None **Twitter handle:** @jonzeolla --------- Co-authored-by: Bagatur <baskaryan@gmail.com>
2025-06-24 07:35:18 +00:00 · 2024-09-02 18:10:21 -04:00 · 2024-09-02 18:10:21 -04:00 · 78ff51ce83
commit 78ff51ce83
parent 150251fd49
19 changed files with 81 additions and 45 deletions
--- a/docs/docs/how_to/embed_text.mdx
+++ b/docs/docs/how_to/embed_text.mdx
@ -94,15 +94,6 @@ from langchain_huggingface import HuggingFaceEmbeddings

 embeddings_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
 ```
-
-You can also leave the `model_name` blank to use the default [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) model.
-
-```python
-from langchain_huggingface import HuggingFaceEmbeddings
-
-embeddings_model = HuggingFaceEmbeddings()
-```
-
  </TabItem>
 </Tabs>

--- a/docs/docs/integrations/platforms/huggingface.mdx
+++ b/docs/docs/integrations/platforms/huggingface.mdx
@ -54,7 +54,7 @@ from langchain_community.embeddings import HuggingFaceInstructEmbeddings

 ### HuggingFaceBgeEmbeddings

->[BGE models on the HuggingFace](https://huggingface.co/BAAI/bge-large-en) are [the best open-source embedding models](https://huggingface.co/spaces/mteb/leaderboard).
+>[BGE models on the HuggingFace](https://huggingface.co/BAAI/bge-large-en-v1.5) are one of [the best open-source embedding models](https://huggingface.co/spaces/mteb/leaderboard).
 >BGE model is created by the [Beijing Academy of Artificial Intelligence (BAAI)](https://en.wikipedia.org/wiki/Beijing_Academy_of_Artificial_Intelligence). `BAAI` is a private non-profit organization engaged in AI research and development.

 See a [usage example](/docs/integrations/text_embedding/bge_huggingface).
--- a/docs/docs/integrations/providers/vdms.mdx
+++ b/docs/docs/integrations/providers/vdms.mdx
@ -44,11 +44,12 @@ from langchain_community.vectorstores.vdms import VDMS_Client
 from langchain_huggingface import HuggingFaceEmbeddings

 client = VDMS_Client("localhost", 55555)
+model_name = "sentence-transformers/all-mpnet-base-v2"
 vectorstore = VDMS.from_documents(
    docs,
    client=client,
    collection_name="langchain-demo",
-    embedding_function=HuggingFaceEmbeddings(),
+    embedding_function=HuggingFaceEmbeddings(model_name=model_name),
    engine="FaissFlat"
    distance_strategy="L2",
 )
@ -58,5 +59,3 @@ results = vectorstore.similarity_search(query)
 ```

 For a more detailed walkthrough of the VDMS wrapper, see [this notebook](/docs/integrations/vectorstores/vdms)
-
-
--- a/docs/docs/integrations/text_embedding/bge_huggingface.ipynb
+++ b/docs/docs/integrations/text_embedding/bge_huggingface.ipynb
@ -7,7 +7,7 @@
   "source": [
    "# BGE on Hugging Face\n",
    "\n",
-    ">[BGE models on the HuggingFace](https://huggingface.co/BAAI/bge-large-en) are [the best open-source embedding models](https://huggingface.co/spaces/mteb/leaderboard).\n",
+    ">[BGE models on the HuggingFace](https://huggingface.co/BAAI/bge-large-en-v1.5) are one of [the best open-source embedding models](https://huggingface.co/spaces/mteb/leaderboard).\n",
    ">BGE model is created by the [Beijing Academy of Artificial Intelligence (BAAI)](https://en.wikipedia.org/wiki/Beijing_Academy_of_Artificial_Intelligence). `BAAI` is a private non-profit organization engaged in AI research and development.\n",
    "\n",
    "This notebook shows how to use `BGE Embeddings` through `Hugging Face`"
--- a/docs/docs/integrations/text_embedding/huggingfacehub.ipynb
+++ b/docs/docs/integrations/text_embedding/huggingfacehub.ipynb
@ -36,7 +36,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "embeddings = HuggingFaceEmbeddings()"
+    "embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\")"
   ]
  },
  {
--- a/docs/docs/integrations/vectorstores/annoy.ipynb
+++ b/docs/docs/integrations/vectorstores/annoy.ipynb
@ -57,7 +57,8 @@
    "from langchain_community.vectorstores import Annoy\n",
    "from langchain_huggingface import HuggingFaceEmbeddings\n",
    "\n",
-    "embeddings_func = HuggingFaceEmbeddings()"
+    "model_name = \"sentence-transformers/all-mpnet-base-v2\"\n",
+    "embeddings_func = HuggingFaceEmbeddings(model_name=model_name)"
   ]
  },
  {
--- a/docs/docs/integrations/vectorstores/scann.ipynb
+++ b/docs/docs/integrations/vectorstores/scann.ipynb
@ -61,7 +61,8 @@
    "docs = text_splitter.split_documents(documents)\n",
    "\n",
    "\n",
-    "embeddings = HuggingFaceEmbeddings()\n",
+    "model_name = \"sentence-transformers/all-mpnet-base-v2\"\n",
+    "embeddings = HuggingFaceEmbeddings(model_name=model_name)\n",
    "\n",
    "db = ScaNN.from_documents(docs, embeddings)\n",
    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
--- a/docs/docs/integrations/vectorstores/semadb.ipynb
+++ b/docs/docs/integrations/vectorstores/semadb.ipynb
@ -45,7 +45,8 @@
   "source": [
    "from langchain_huggingface import HuggingFaceEmbeddings\n",
    "\n",
-    "embeddings = HuggingFaceEmbeddings()"
+    "model_name = \"sentence-transformers/all-mpnet-base-v2\"\n",
+    "embeddings = HuggingFaceEmbeddings(model_name=model_name)"
   ]
  },
  {
--- a/docs/docs/integrations/vectorstores/surrealdb.ipynb
+++ b/docs/docs/integrations/vectorstores/surrealdb.ipynb
@ -92,7 +92,8 @@
    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
    "docs = text_splitter.split_documents(documents)\n",
    "\n",
-    "embeddings = HuggingFaceEmbeddings()"
+    "model_name = \"sentence-transformers/all-mpnet-base-v2\"\n",
+    "embeddings = HuggingFaceEmbeddings(model_name=model_name)"
   ]
  },
  {
--- a/docs/docs/integrations/vectorstores/tiledb.ipynb
+++ b/docs/docs/integrations/vectorstores/tiledb.ipynb
@ -51,7 +51,8 @@
    "raw_documents = TextLoader(\"../../how_to/state_of_the_union.txt\").load()\n",
    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
    "documents = text_splitter.split_documents(raw_documents)\n",
-    "embeddings = HuggingFaceEmbeddings()\n",
+    "model_name = \"sentence-transformers/all-mpnet-base-v2\"\n",
+    "embeddings = HuggingFaceEmbeddings(model_name=model_name)\n",
    "db = TileDB.from_documents(\n",
    "    documents, embeddings, index_uri=\"/tmp/tiledb_index\", index_type=\"FLAT\"\n",
    ")"
--- a/docs/docs/integrations/vectorstores/vald.ipynb
+++ b/docs/docs/integrations/vectorstores/vald.ipynb
@ -50,7 +50,8 @@
    "raw_documents = TextLoader(\"state_of_the_union.txt\").load()\n",
    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
    "documents = text_splitter.split_documents(raw_documents)\n",
-    "embeddings = HuggingFaceEmbeddings()\n",
+    "model_name = \"sentence-transformers/all-mpnet-base-v2\"\n",
+    "embeddings = HuggingFaceEmbeddings(model_name=model_name)\n",
    "db = Vald.from_documents(documents, embeddings, host=\"localhost\", port=8080)"
   ]
  },
@ -197,7 +198,8 @@
    "raw_documents = TextLoader(\"state_of_the_union.txt\").load()\n",
    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
    "documents = text_splitter.split_documents(raw_documents)\n",
-    "embeddings = HuggingFaceEmbeddings()\n",
+    "model_name = \"sentence-transformers/all-mpnet-base-v2\"\n",
+    "embeddings = HuggingFaceEmbeddings(model_name=model_name)\n",
    "\n",
    "db = Vald.from_documents(\n",
    "    documents,\n",
--- a/docs/docs/integrations/vectorstores/vdms.ipynb
+++ b/docs/docs/integrations/vectorstores/vdms.ipynb
@ -200,7 +200,8 @@
    "\n",
    "\n",
    "# create the open-source embedding function\n",
-    "embedding = HuggingFaceEmbeddings()\n",
+    "model_name = \"sentence-transformers/all-mpnet-base-v2\"\n",
+    "embedding = HuggingFaceEmbeddings(model_name=model_name)\n",
    "print(\n",
    "    f\"# Embedding Dimensions: {len(embedding.embed_query('This is a test document.'))}\"\n",
    ")"
--- a/libs/community/langchain_community/embeddings/huggingface.py
+++ b/libs/community/langchain_community/embeddings/huggingface.py
@ -67,6 +67,19 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
    def __init__(self, **kwargs: Any):
        """Initialize the sentence_transformer."""
        super().__init__(**kwargs)
+
+        if "model_name" not in kwargs:
+            since = "0.2.16"
+            removal = "0.4.0"
+            warn_deprecated(
+                since=since,
+                removal=removal,
+                message=f"Default values for {self.__class__.__name__}.model_name"
+                + f" were deprecated in LangChain {since} and will be removed in"
+                + f" {removal}. Explicitly pass a model_name to the"
+                + f" {self.__class__.__name__} constructor instead.",
+            )
+
        try:
            import sentence_transformers

@ -159,6 +172,19 @@ class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):
    def __init__(self, **kwargs: Any):
        """Initialize the sentence_transformer."""
        super().__init__(**kwargs)
+
+        if "model_name" not in kwargs:
+            since = "0.2.16"
+            removal = "0.4.0"
+            warn_deprecated(
+                since=since,
+                removal=removal,
+                message=f"Default values for {self.__class__.__name__}.model_name"
+                + f" were deprecated in LangChain {since} and will be removed in"
+                + f" {removal}. Explicitly pass a model_name to the"
+                + f" {self.__class__.__name__} constructor instead.",
+            )
+
        try:
            from InstructorEmbedding import INSTRUCTOR

@ -231,7 +257,7 @@ class HuggingFaceBgeEmbeddings(BaseModel, Embeddings):

            from langchain_community.embeddings import HuggingFaceBgeEmbeddings

-            model_name = "BAAI/bge-large-en"
+            model_name = "BAAI/bge-large-en-v1.5"
            model_kwargs = {'device': 'cpu'}
            encode_kwargs = {'normalize_embeddings': True}
            hf = HuggingFaceBgeEmbeddings(
@ -279,6 +305,19 @@ class HuggingFaceBgeEmbeddings(BaseModel, Embeddings):
    def __init__(self, **kwargs: Any):
        """Initialize the sentence_transformer."""
        super().__init__(**kwargs)
+
+        if "model_name" not in kwargs:
+            since = "0.2.5"
+            removal = "0.4.0"
+            warn_deprecated(
+                since=since,
+                removal=removal,
+                message=f"Default values for {self.__class__.__name__}.model_name"
+                + f" were deprecated in LangChain {since} and will be removed in"
+                + f" {removal}. Explicitly pass a model_name to the"
+                + f" {self.__class__.__name__} constructor instead.",
+            )
+
        try:
            import sentence_transformers

--- a/libs/community/langchain_community/embeddings/openvino.py
+++ b/libs/community/langchain_community/embeddings/openvino.py
@ -303,7 +303,7 @@ class OpenVINOBgeEmbeddings(OpenVINOEmbeddings):

            from langchain_community.embeddings import OpenVINOBgeEmbeddings

-            model_name = "BAAI/bge-large-en"
+            model_name = "BAAI/bge-large-en-v1.5"
            model_kwargs = {'device': 'CPU'}
            encode_kwargs = {'normalize_embeddings': True}
            ov = OpenVINOBgeEmbeddings(
--- a/libs/community/langchain_community/vectorstores/scann.py
+++ b/libs/community/langchain_community/vectorstores/scann.py
@ -41,9 +41,10 @@ class ScaNN(VectorStore):
            from langchain_community.embeddings import HuggingFaceEmbeddings
            from langchain_community.vectorstores import ScaNN

+            model_name = "sentence-transformers/all-mpnet-base-v2"
            db = ScaNN.from_texts(
                ['foo', 'bar', 'barz', 'qux'],
-                HuggingFaceEmbeddings())
+                HuggingFaceEmbeddings(model_name=model_name))
            db.similarity_search('foo?', k=1)
    """

--- a/libs/community/langchain_community/vectorstores/surrealdb.py
+++ b/libs/community/langchain_community/vectorstores/surrealdb.py
@ -1,12 +1,5 @@
 import asyncio
-from typing import (
-    Any,
-    Dict,
-    Iterable,
-    List,
-    Optional,
-    Tuple,
-)
+from typing import Any, Dict, Iterable, List, Optional, Tuple

 import numpy as np
 from langchain_core.documents import Document
@ -40,7 +33,8 @@ class SurrealDBStore(VectorStore):
            from langchain_community.vectorstores.surrealdb import SurrealDBStore
            from langchain_community.embeddings import HuggingFaceEmbeddings

-            embedding_function = HuggingFaceEmbeddings()
+            model_name = "sentence-transformers/all-mpnet-base-v2"
+            embedding_function = HuggingFaceEmbeddings(model_name=model_name)
            dburl = "ws://localhost:8000/rpc"
            ns = "langchain"
            db = "docstore"
--- a/libs/community/langchain_community/vectorstores/vald.py
+++ b/libs/community/langchain_community/vectorstores/vald.py
@ -23,10 +23,11 @@ class Vald(VectorStore):
            from langchain_community.embeddings import HuggingFaceEmbeddings
            from langchain_community.vectorstores import Vald

+            model_name = "sentence-transformers/all-mpnet-base-v2"
            texts = ['foo', 'bar', 'baz']
            vald = Vald.from_texts(
                texts=texts,
-                embedding=HuggingFaceEmbeddings(),
+                embedding=HuggingFaceEmbeddings(model_name=model_name),
                host="localhost",
                port=8080,
                skip_strict_exist_check=False,
--- a/libs/community/langchain_community/vectorstores/vdms.py
+++ b/libs/community/langchain_community/vectorstores/vdms.py
@ -161,9 +161,10 @@ class VDMS(VectorStore):
            from langchain_huggingface import HuggingFaceEmbeddings
            from langchain_community.vectorstores.vdms import VDMS, VDMS_Client

+            model_name = "sentence-transformers/all-mpnet-base-v2"
            vectorstore = VDMS(
                client=VDMS_Client("localhost", 55555),
-                embedding=HuggingFaceEmbeddings(),
+                embedding=HuggingFaceEmbeddings(model_name=model_name),
                collection_name="langchain-demo",
                distance_strategy="L2",
                engine="FaissFlat",
--- a/templates/self-query-qdrant/README.md
+++ b/templates/self-query-qdrant/README.md
@ -92,9 +92,10 @@ from langchain.chains.query_constructor.schema import AttributeInfo

 from self_query_qdrant.chain import create_chain

+model_name = "sentence-transformers/all-mpnet-base-v2"
 chain = create_chain(
    llm=Cohere(),
-    embeddings=HuggingFaceEmbeddings(),
+    embeddings=HuggingFaceEmbeddings(model_name=model_name),
    document_contents="Descriptions of cats, along with their names and breeds.",
    metadata_field_info=[
        AttributeInfo(name="name", description="Name of the cat", type="string"),
@ -112,8 +113,9 @@ from langchain_community.embeddings import HuggingFaceEmbeddings

 from self_query_qdrant.chain import initialize

+model_name = "sentence-transformers/all-mpnet-base-v2"
 initialize(
-    embeddings=HuggingFaceEmbeddings(),
+    embeddings=HuggingFaceEmbeddings(model_name=model_name),
    collection_name="cats",
    documents=[
        Document(