diff --git a/.github/workflows/python.yml b/.github/workflows/python.yml
index b837a633f..ec232dcfc 100644
--- a/.github/workflows/python.yml
+++ b/.github/workflows/python.yml
@@ -195,31 +195,3 @@ jobs:
       # Make sure wheels are not included in the Rust cache
       - name: Delete wheels
         run: rm -rf target/wheels
-  pydantic1x:
-    timeout-minutes: 30
-    runs-on: "ubuntu-24.04"
-    defaults:
-      run:
-        shell: bash
-        working-directory: python
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          lfs: true
-      - name: Install dependencies
-        run: |
-          sudo apt update
-          sudo apt install -y protobuf-compiler
-      - name: Set up Python
-        uses: actions/setup-python@v6
-        with:
-          python-version: "3.10"
-      - name: Install lancedb
-        run: |
-          pip install "pydantic<2"
-          pip install pyarrow==16
-          pip install --extra-index-url https://pypi.fury.io/lance-format/ --extra-index-url https://pypi.fury.io/lancedb/ -e .[tests]
-          pip install tantivy
-      - name: Run tests
-        run: pytest -m "not slow and not s3_test" -x -v --durations=30 python/tests
diff --git a/python/pyproject.toml b/python/pyproject.toml
index b6fe79b9c..f5517fe2b 100644
--- a/python/pyproject.toml
+++ b/python/pyproject.toml
@@ -8,7 +8,7 @@ dependencies = [
     "overrides>=0.7; python_version<'3.12'",
     "packaging",
     "pyarrow>=16",
-    "pydantic>=1.10",
+    "pydantic>=2",
     "tqdm>=4.27.0",
     "lance-namespace>=0.3.2"
 ]
diff --git a/python/python/lancedb/pydantic.py b/python/python/lancedb/pydantic.py
index 653ea3333..9496aebdd 100644
--- a/python/python/lancedb/pydantic.py
+++ b/python/python/lancedb/pydantic.py
@@ -141,14 +141,6 @@ def Vector(
                 raise TypeError("A list of numbers or numpy.ndarray is needed")
             return cls(v)
 
-        if PYDANTIC_VERSION.major < 2:
-
-            @classmethod
-            def __modify_schema__(cls, field_schema: Dict[str, Any]):
-                field_schema["items"] = {"type": "number"}
-                field_schema["maxItems"] = dim
-                field_schema["minItems"] = dim
-
     return FixedSizeList
 
 
@@ -226,26 +218,14 @@ def MultiVector(
         def __get_validators__(cls) -> Generator[Callable, None, None]:
             yield cls.validate
 
-        # For pydantic v1
         @classmethod
-        def validate(cls, v):
-            if not isinstance(v, (list, range)):
-                raise TypeError("A list of vectors is needed")
-            for vec in v:
-                if not isinstance(vec, (list, range, np.ndarray)) or len(vec) != dim:
-                    raise TypeError(f"Each vector must be a list of {dim} numbers")
-            return cls(v)
-
-        if PYDANTIC_VERSION.major < 2:
-
-            @classmethod
-            def __modify_schema__(cls, field_schema: Dict[str, Any]):
-                field_schema["items"] = {
-                    "type": "array",
-                    "items": {"type": "number"},
-                    "minItems": dim,
-                    "maxItems": dim,
-                }
+        def __modify_schema__(cls, field_schema: Dict[str, Any]):
+            field_schema["items"] = {
+                "type": "array",
+                "items": {"type": "number"},
+                "minItems": dim,
+                "maxItems": dim,
+            }
 
     return MultiVectorList
 
@@ -281,20 +261,10 @@ def _py_type_to_arrow_type(py_type: Type[Any], field: FieldInfo) -> pa.DataType:
     )
 
 
-if PYDANTIC_VERSION.major < 2:
-
-    def _pydantic_model_to_fields(model: pydantic.BaseModel) -> List[pa.Field]:
-        return [
-            _pydantic_to_field(name, field) for name, field in model.__fields__.items()
-        ]
-
-else:
-
-    def _pydantic_model_to_fields(model: pydantic.BaseModel) -> List[pa.Field]:
-        return [
-            _pydantic_to_field(name, field)
-            for name, field in model.model_fields.items()
-        ]
+def _pydantic_model_to_fields(model: pydantic.BaseModel) -> List[pa.Field]:
+    return [
+        _pydantic_to_field(name, field) for name, field in model.model_fields.items()
+    ]
 
 
 def _pydantic_type_to_arrow_type(tp: Any, field: FieldInfo) -> pa.DataType:
@@ -334,7 +304,7 @@ def _unwrap_optional_annotation(annotation: Any) -> Any | None:
             non_none = [arg for arg in args if arg is not type(None)]
             if len(non_none) == 1 and len(non_none) != len(args):
                 return non_none[0]
-    elif sys.version_info >= (3, 10) and isinstance(annotation, types.UnionType):
+    elif isinstance(annotation, types.UnionType):
         args = annotation.__args__
         non_none = [arg for arg in args if arg is not type(None)]
         if len(non_none) == 1 and len(non_none) != len(args):
@@ -367,7 +337,7 @@ def is_nullable(field: FieldInfo) -> bool:
         if origin == Union:
             if any(typ is type(None) for typ in args):
                 return True
-    elif sys.version_info >= (3, 10) and isinstance(field.annotation, types.UnionType):
+    elif isinstance(field.annotation, types.UnionType):
         args = field.annotation.__args__
         for typ in args:
             if typ is type(None):
@@ -474,8 +444,6 @@ class LanceModel(pydantic.BaseModel):
 
     @classmethod
     def safe_get_fields(cls):
-        if PYDANTIC_VERSION.major < 2:
-            return cls.__fields__
         return cls.model_fields
 
     @classmethod
@@ -518,18 +486,8 @@ def get_extras(field_info: FieldInfo, key: str) -> Any:
     return (field_info.field_info.extra or {}).get("json_schema_extra", {}).get(key)
 
 
-if PYDANTIC_VERSION.major < 2:
-
-    def model_to_dict(model: pydantic.BaseModel) -> Dict[str, Any]:
-        """
-        Convert a Pydantic model to a dictionary.
-        """
-        return model.dict()
-
-else:
-
-    def model_to_dict(model: pydantic.BaseModel) -> Dict[str, Any]:
-        """
-        Convert a Pydantic model to a dictionary.
-        """
-        return model.model_dump()
+def model_to_dict(model: pydantic.BaseModel) -> Dict[str, Any]:
+    """
+    Convert a Pydantic model to a dictionary.
+    """
+    return model.model_dump()