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/retrievers/knn.py

@@ -12,6 +12,7 @@ from langchain_core.callbacks import CallbackManagerForRetrieverRun
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
 from langchain_core.retrievers import BaseRetriever
+from pydantic import ConfigDict
 
 
 def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray:
@@ -34,7 +35,7 @@ class KNNRetriever(BaseRetriever):
 
     embeddings: Embeddings
     """Embeddings model to use."""
-    index: Any
+    index: Any = None
     """Index of embeddings."""
     texts: List[str]
     """List of texts to index."""
@@ -45,8 +46,9 @@ class KNNRetriever(BaseRetriever):
     relevancy_threshold: Optional[float] = None
     """Threshold for relevancy."""
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+    )
 
     @classmethod
     def from_texts(