diff --git a/poetry.lock b/poetry.lock
index d6c0340d..668ba3e8 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -5562,4 +5562,4 @@ vision = ["pillow", "torch", "torchvision", "transformers"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.14"
-content-hash = "e88cbb248e65005bf3265b1ef31c768baf6f979d08e829458163cb567e09ffa3"
+content-hash = "7a8c31e645e649de60985ecb0ddec1aefcdeb1cfcbe23b47185a0bd18407a900"
diff --git a/pyproject.toml b/pyproject.toml
index 5a52b774..a6b71d9c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,7 +9,7 @@ license = "MIT"
 
 [tool.poetry.dependencies]
 python = ">=3.9,<3.14"
-pydantic = "^2.5.3"
+pydantic = "^2.10.2"
 openai = ">=1.10.0,<2.0.0"
 cohere = {version = ">=5.9.4,<6.00", optional = true}
 mistralai= {version = ">=0.0.12,<0.1.0", optional = true}
diff --git a/semantic_router/encoders/base.py b/semantic_router/encoders/base.py
index 0a25f210..993093c1 100644
--- a/semantic_router/encoders/base.py
+++ b/semantic_router/encoders/base.py
@@ -1,6 +1,6 @@
 from typing import Any, Coroutine, List, Optional
 
-from pydantic.v1 import BaseModel, Field, validator
+from pydantic import BaseModel, Field, field_validator
 import numpy as np
 
 from semantic_router.schema import SparseEmbedding
@@ -14,7 +14,7 @@ class DenseEncoder(BaseModel):
     class Config:
         arbitrary_types_allowed = True
 
-    @validator("score_threshold", pre=True, always=True)
+    @field_validator("score_threshold")
     def set_score_threshold(cls, v):
         return float(v) if v is not None else None
 
diff --git a/semantic_router/encoders/clip.py b/semantic_router/encoders/clip.py
index 65fbdb8f..d30f5e1f 100644
--- a/semantic_router/encoders/clip.py
+++ b/semantic_router/encoders/clip.py
@@ -9,7 +9,6 @@
 class CLIPEncoder(DenseEncoder):
     name: str = "openai/clip-vit-base-patch16"
     type: str = "huggingface"
-    score_threshold: float = 0.2
     tokenizer_kwargs: Dict = {}
     processor_kwargs: Dict = {}
     model_kwargs: Dict = {}
@@ -21,6 +20,8 @@ class CLIPEncoder(DenseEncoder):
     _Image: Any = PrivateAttr()
 
     def __init__(self, **data):
+        if data.get("score_threshold") is None:
+            data["score_threshold"] = 0.2
         super().__init__(**data)
         self._tokenizer, self._processor, self._model = self._initialize_hf_model()
 
diff --git a/semantic_router/encoders/huggingface.py b/semantic_router/encoders/huggingface.py
index 7c7e56f7..c791e1c3 100644
--- a/semantic_router/encoders/huggingface.py
+++ b/semantic_router/encoders/huggingface.py
@@ -34,7 +34,6 @@
 class HuggingFaceEncoder(DenseEncoder):
     name: str = "sentence-transformers/all-MiniLM-L6-v2"
     type: str = "huggingface"
-    score_threshold: float = 0.5
     tokenizer_kwargs: Dict = {}
     model_kwargs: Dict = {}
     device: Optional[str] = None
@@ -43,6 +42,8 @@ class HuggingFaceEncoder(DenseEncoder):
     _torch: Any = PrivateAttr()
 
     def __init__(self, **data):
+        if data.get("score_threshold") is None:
+            data["score_threshold"] = 0.5
         super().__init__(**data)
         self._tokenizer, self._model = self._initialize_hf_model()
 
@@ -153,7 +154,6 @@ class HFEndpointEncoder(DenseEncoder):
     name: str = "hugging_face_custom_endpoint"
     huggingface_url: Optional[str] = None
     huggingface_api_key: Optional[str] = None
-    score_threshold: float = 0.8
 
     def __init__(
         self,
@@ -180,6 +180,8 @@ def __init__(
         """
         huggingface_url = huggingface_url or os.getenv("HF_API_URL")
         huggingface_api_key = huggingface_api_key or os.getenv("HF_API_KEY")
+        if score_threshold is None:
+            score_threshold = 0.8
 
         super().__init__(name=name, score_threshold=score_threshold)  # type: ignore
 
diff --git a/semantic_router/encoders/vit.py b/semantic_router/encoders/vit.py
index 73cb0582..dec768e4 100644
--- a/semantic_router/encoders/vit.py
+++ b/semantic_router/encoders/vit.py
@@ -1,6 +1,6 @@
-from typing import Any, List, Optional, Dict
+from typing import Any, Dict, List, Optional
 
-from pydantic.v1 import PrivateAttr
+from pydantic import PrivateAttr
 
 from semantic_router.encoders import DenseEncoder
 
@@ -8,7 +8,6 @@
 class VitEncoder(DenseEncoder):
     name: str = "google/vit-base-patch16-224"
     type: str = "huggingface"
-    score_threshold: float = 0.5
     processor_kwargs: Dict = {}
     model_kwargs: Dict = {}
     device: Optional[str] = None
@@ -19,6 +18,8 @@ class VitEncoder(DenseEncoder):
     _Image: Any = PrivateAttr()
 
     def __init__(self, **data):
+        if data.get("score_threshold") is None:
+            data["score_threshold"] = 0.5
         super().__init__(**data)
         self._processor, self._model = self._initialize_hf_model()
 
diff --git a/semantic_router/index/postgres.py b/semantic_router/index/postgres.py
index 6d445f2b..71ea32e8 100644
--- a/semantic_router/index/postgres.py
+++ b/semantic_router/index/postgres.py
@@ -103,7 +103,6 @@ class PostgresIndex(BaseIndex):
     connection_string: Optional[str] = None
     index_prefix: str = "semantic_router_"
     index_name: str = "index"
-    dimensions: int = 1536
     metric: Metric = Metric.COSINE
     namespace: Optional[str] = ""
     conn: Optional["psycopg2.extensions.connection"] = None
@@ -115,9 +114,9 @@ def __init__(
         connection_string: Optional[str] = None,
         index_prefix: str = "semantic_router_",
         index_name: str = "index",
-        dimensions: int = 1536,
         metric: Metric = Metric.COSINE,
         namespace: Optional[str] = "",
+        dimensions: int | None = None,
     ):
         """
         Initializes the Postgres index with the specified parameters.
@@ -135,6 +134,8 @@ def __init__(
         :param namespace: An optional namespace for the index.
         :type namespace: Optional[str]
         """
+        if dimensions is None:
+            dimensions = 1536
         super().__init__()
         # try and import psycopg2
         try: