diff --git a/application/vectorstore/faiss.py b/application/vectorstore/faiss.py
index a8839cd2..ee74b971 100644
--- a/application/vectorstore/faiss.py
+++ b/application/vectorstore/faiss.py
@@ -3,30 +3,27 @@ from application.vectorstore.base import BaseVectorStore
 from application.core.settings import settings
 import os
 
-def get_vectorstore(path):
+def get_vectorstore(path: str) -> str:
     if path:
-        vectorstore = "indexes/"+path
-        vectorstore = os.path.join("application", vectorstore)
+        vectorstore = os.path.join("application", "indexes", path)
     else:
         vectorstore = os.path.join("application")
-
     return vectorstore
 
 class FaissStore(BaseVectorStore):
-
-    def __init__(self, source_id, embeddings_key, docs_init=None):
+    def __init__(self, source_id: str, embeddings_key: str, docs_init=None):
         super().__init__()
         self.path = get_vectorstore(source_id)
         embeddings = self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key)
-        if docs_init:
-            self.docsearch = FAISS.from_documents(
-                docs_init, embeddings
-            )
-        else:
-            self.docsearch = FAISS.load_local(
-                self.path, embeddings, 
-                allow_dangerous_deserialization=True
-            )
+
+        try:
+            if docs_init:
+                self.docsearch = FAISS.from_documents(docs_init, embeddings)
+            else:
+                self.docsearch = FAISS.load_local(self.path, embeddings, allow_dangerous_deserialization=True)
+        except Exception as e:
+            raise
+
         self.assert_embedding_dimensions(embeddings)
 
     def search(self, *args, **kwargs):
@@ -42,16 +39,12 @@ class FaissStore(BaseVectorStore):
         return self.docsearch.delete(*args, **kwargs)
 
     def assert_embedding_dimensions(self, embeddings):
-        """
-        Check that the word embedding dimension of the docsearch index matches
-        the dimension of the word embeddings used 
-        """
+        """Check that the word embedding dimension of the docsearch index matches the dimension of the word embeddings used."""
         if settings.EMBEDDINGS_NAME == "huggingface_sentence-transformers/all-mpnet-base-v2":
-            try:
-                word_embedding_dimension = embeddings.dimension
-            except AttributeError as e:
-                raise AttributeError("'dimension' attribute not found in embeddings instance. Make sure the embeddings object is properly initialized.") from e
+            word_embedding_dimension = getattr(embeddings, 'dimension', None)
+            if word_embedding_dimension is None:
+                raise AttributeError("'dimension' attribute not found in embeddings instance.")
+            
             docsearch_index_dimension = self.docsearch.index.d
             if word_embedding_dimension != docsearch_index_dimension:
-                raise ValueError(f"Embedding dimension mismatch: embeddings.dimension ({word_embedding_dimension}) " +
-                                 f"!= docsearch index dimension ({docsearch_index_dimension})")
\ No newline at end of file
+                raise ValueError(f"Embedding dimension mismatch: embeddings.dimension ({word_embedding_dimension}) != docsearch index dimension ({docsearch_index_dimension})")