diff --git a/lmm_tools/data/data.py b/lmm_tools/data/data.py
index 7357b6c5..533b5a70 100644
--- a/lmm_tools/data/data.py
+++ b/lmm_tools/data/data.py
@@ -2,6 +2,7 @@
 
 import uuid
 from pathlib import Path
+from typing import Dict, List, Optional, Union
 
 import faiss
 import numpy as np
@@ -28,8 +29,8 @@ def __init__(self, df: pd.DataFrame):
 
         """
         self.df = df
-        self.lmm: LMM | None = None
-        self.emb: Embedder | None = None
+        self.lmm: Optional[LMM] = None
+        self.emb: Optional[Embedder] = None
         self.index = None
         if "image_paths" not in self.df.columns:
             raise ValueError("image_paths column must be present in DataFrame")
@@ -75,7 +76,7 @@ def get_embeddings(self) -> npt.NDArray[np.float32]:
         d = self.index.d
         return faiss.rev_swig_ptr(self.index.get_xb(), ntotal * d).reshape(ntotal, d)
 
-    def search(self, query: str, top_k: int = 10) -> list[dict]:
+    def search(self, query: str, top_k: int = 10) -> List[Dict]:
         r"""Searches the index for the most similar images to the query and returns the top_k results.
         Args:
             query (str): The query to search for.
@@ -89,7 +90,7 @@ def search(self, query: str, top_k: int = 10) -> list[dict]:
         _, idx = self.index.search(query_embedding.reshape(1, -1), top_k)
         return self.df.iloc[idx[0]].to_dict(orient="records")
 
-    def save(self, path: str | Path) -> None:
+    def save(self, path: Union[str, Path]) -> None:
         path = Path(path)
         path.mkdir(parents=True)
         self.df.to_csv(path / "data.csv")
@@ -97,7 +98,7 @@ def save(self, path: str | Path) -> None:
             write_index(self.index, str(path / "data.index"))
 
     @classmethod
-    def load(cls, path: str | Path) -> DataStore:
+    def load(cls, path: Union[str, Path]) -> DataStore:
         path = Path(path)
         df = pd.read_csv(path / "data.csv", index_col=0)
         ds = DataStore(df)
@@ -106,7 +107,7 @@ def load(cls, path: str | Path) -> DataStore:
         return ds
 
 
-def build_data_store(data: str | Path | list[str | Path]) -> DataStore:
+def build_data_store(data: Union[str, Path, list[Union[str, Path]]]) -> DataStore:
     if isinstance(data, Path) or isinstance(data, str):
         data = Path(data)
         data_files = list(Path(data).glob("*"))
diff --git a/lmm_tools/lmm/lmm.py b/lmm_tools/lmm/lmm.py
index 4f20c7f3..e4c80032 100644
--- a/lmm_tools/lmm/lmm.py
+++ b/lmm_tools/lmm/lmm.py
@@ -1,7 +1,7 @@
 import base64
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Optional
+from typing import Optional, Union
 
 
 def encode_image(image: str | Path) -> str:
@@ -12,7 +12,7 @@ def encode_image(image: str | Path) -> str:
 
 class LMM(ABC):
     @abstractmethod
-    def generate(self, prompt: str, image: Optional[str | Path]) -> str:
+    def generate(self, prompt: str, image: Optional[Union[str, Path]]) -> str:
         pass
 
 
@@ -22,7 +22,7 @@ class LLaVALMM(LMM):
     def __init__(self, name: str):
         self.name = name
 
-    def generate(self, prompt: str, image: Optional[str | Path]) -> str:
+    def generate(self, prompt: str, image: Optional[Union[str, Path]]) -> str:
         pass
 
 
@@ -35,7 +35,7 @@ def __init__(self, name: str):
         self.name = name
         self.client = OpenAI()
 
-    def generate(self, prompt: str, image: Optional[str | Path]) -> str:
+    def generate(self, prompt: str, image: Optional[Union[str, Path]]) -> str:
         message = [
             {
                 "role": "user",