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/community/langchain_community/embeddings/sparkllm.py

@@ -12,11 +12,11 @@ from wsgiref.handlers import format_date_time
 import numpy as np
 import requests
 from langchain_core.embeddings import Embeddings
-from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr
 from langchain_core.utils import (
     secret_from_env,
 )
 from numpy import ndarray
+from pydantic import BaseModel, ConfigDict, Field, SecretStr
 
 # SparkLLMTextEmbeddings is an embedding model provided by iFLYTEK Co., Ltd.. (https://iflytek.com/en/).
 
@@ -124,8 +124,9 @@ class SparkLLMTextEmbeddings(BaseModel, Embeddings):
     If "para"(default), it belongs to document Embedding. 
     If "query", it belongs to query Embedding."""
 
-    class Config:
-        allow_population_by_field_name = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+    )
 
     def _embed(self, texts: List[str], host: str) -> Optional[List[List[float]]]:
         """Internal method to call Spark Embedding API and return embeddings.