multiple: pydantic 2 compatibility, v0.3 (#26443)

Signed-off-by: ChengZi <chen.zhang@zilliz.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Dan O'Donovan <dan.odonovan@gmail.com>
Co-authored-by: Tom Daniel Grande <tomdgrande@gmail.com>
Co-authored-by: Grande <Tom.Daniel.Grande@statsbygg.no>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: ccurme <chester.curme@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Tomaz Bratanic <bratanic.tomaz@gmail.com>
Co-authored-by: ZhangShenao <15201440436@163.com>
Co-authored-by: Friso H. Kingma <fhkingma@gmail.com>
Co-authored-by: ChengZi <chen.zhang@zilliz.com>
Co-authored-by: Nuno Campos <nuno@langchain.dev>
Co-authored-by: Morgante Pell <morgantep@google.com>

libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py

@@ -1,7 +1,7 @@
 from typing import Any, Dict, List, Optional  # type: ignore[import-not-found]
 
 from langchain_core.embeddings import Embeddings
-from langchain_core.pydantic_v1 import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
 
@@ -26,7 +26,7 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
             )
     """
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     model_name: str = DEFAULT_MODEL_NAME
     """Model name to use."""
     cache_folder: Optional[str] = None
@@ -51,7 +51,6 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
         super().__init__(**kwargs)
         try:
             import sentence_transformers  # type: ignore[import]
-
         except ImportError as exc:
             raise ImportError(
                 "Could not import sentence_transformers python package. "
@@ -62,10 +61,10 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
             self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
         )
 
-    class Config:
-        """Configuration for this pydantic object."""
-
-        extra = "forbid"
+    model_config = ConfigDict(
+        extra="forbid",
+        protected_namespaces=(),
+    )
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
         """Compute doc embeddings using a HuggingFace transformer model.