diff --git a/application/requirements.txt b/application/requirements.txt
index 6ffad8ec..9c60e421 100644
--- a/application/requirements.txt
+++ b/application/requirements.txt
@@ -104,3 +104,4 @@ urllib3==1.26.17
 vine==5.0.0
 wcwidth==0.2.6
 yarl==1.8.2
+sentence-transformers==2.2.2
\ No newline at end of file
diff --git a/application/vectorstore/faiss.py b/application/vectorstore/faiss.py
index 217b0457..e8960fe0 100644
--- a/application/vectorstore/faiss.py
+++ b/application/vectorstore/faiss.py
@@ -1,5 +1,5 @@
-from application.vectorstore.base import BaseVectorStore
 from langchain.vectorstores import FAISS
+from application.vectorstore.base import BaseVectorStore
 from application.core.settings import settings
 
 class FaissStore(BaseVectorStore):
@@ -7,20 +7,38 @@ class FaissStore(BaseVectorStore):
     def __init__(self, path, embeddings_key, docs_init=None):
         super().__init__()
         self.path = path
+        embeddings = self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key)
         if docs_init:
             self.docsearch = FAISS.from_documents(
-                docs_init, self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key)
+                docs_init, embeddings
             )
         else:
             self.docsearch = FAISS.load_local(
-                self.path, self._get_embeddings(settings.EMBEDDINGS_NAME, settings.EMBEDDINGS_KEY)
+                self.path, embeddings
             )
+        self.assert_embedding_dimensions(embeddings)
 
     def search(self, *args, **kwargs):
         return self.docsearch.similarity_search(*args, **kwargs)
 
     def add_texts(self, *args, **kwargs):
         return self.docsearch.add_texts(*args, **kwargs)
-    
+
     def save_local(self, *args, **kwargs):
         return self.docsearch.save_local(*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 
+        """
+        if settings.EMBEDDINGS_NAME == "huggingface_sentence-transformers/all-mpnet-base-v2":
+            try:
+                word_embedding_dimension = embeddings.client[1].word_embedding_dimension
+            except AttributeError as e:
+                raise AttributeError("word_embedding_dimension not found in embeddings.client[1]") from e
+            docsearch_index_dimension = self.docsearch.index.d
+            if word_embedding_dimension != docsearch_index_dimension:
+                raise ValueError(f"word_embedding_dimension ({word_embedding_dimension}) " +
+                                 f"!= docsearch_index_word_embedding_dimension ({docsearch_index_dimension})")
+
diff --git a/tests/test_vector_store.py b/tests/test_vector_store.py
new file mode 100644
index 00000000..297a225c
--- /dev/null
+++ b/tests/test_vector_store.py
@@ -0,0 +1,19 @@
+"""
+Tests regarding the vector store class, including checking
+compatibility between different transformers and local vector
+stores (index.faiss)
+"""
+import pytest
+from application.vectorstore.faiss import FaissStore
+from application.core.settings import settings
+
+def test_init_local_faiss_store_huggingface():
+    """
+    Test that asserts that trying to initialize a FaissStore with
+    the huggingface sentence transformer below together with the
+    index.faiss file in the application/ folder results in a
+    dimension mismatch error.
+    """
+    settings.EMBEDDINGS_NAME = "huggingface_sentence-transformers/all-mpnet-base-v2"
+    with pytest.raises(ValueError):
+        FaissStore("application/", "", None)