raise more legible error if the embedding vector dims don't match

application/vectorstore/faiss.py

@@ -2,6 +2,7 @@
 from langchain.vectorstores import FAISS
 from application.core.settings import settings
 
+HUGGINGFACE_MODEL_NAME = "huggingface_sentence-transformers/all-mpnet-base-v2"
 class FaissStore(BaseVectorStore):
 
     def __init__(self, path, embeddings_key, docs_init=None):
@@ -12,9 +13,19 @@ def __init__(self, path, embeddings_key, docs_init=None):
                 docs_init, self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key)
             )
         else:
+            embeddings = self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key)
             self.docsearch = FAISS.load_local(
-                self.path, self._get_embeddings(settings.EMBEDDINGS_NAME, settings.EMBEDDINGS_KEY)
+                self.path, embeddings
             )
+
+            # Check that the word_embedding_dimension of the index matches the word_embedding_dimension of the embeddings
+            if settings.EMBEDDINGS_NAME == HUGGINGFACE_MODEL_NAME:
+                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
+                if word_embedding_dimension != self.docsearch.index.d:
+                    raise ValueError("word_embedding_dimension != docsearch_index_word_embedding_dimension")
 
     def search(self, *args, **kwargs):
         return self.docsearch.similarity_search(*args, **kwargs)