I wasn't properly using the embeddings cache

app.py

@@ -12,6 +12,7 @@ from langchain_core.runnables.passthrough import RunnablePassthrough
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_openai import ChatOpenAI
+import asyncio
 
 print("""
 =================================================================================
@@ -35,13 +36,13 @@ chunked_documents = text_splitter.split_documents(data)
 # Store the chunked documents in the vector store if that was not done already
 embedding_model = OpenAIEmbeddings()
 store = LocalFileStore("./cache/")
-embedder = CacheBackedEmbeddings.from_bytes_store(embedding_model, store, namespace=embedding_model.model)
+cached_embedder = CacheBackedEmbeddings.from_bytes_store(embedding_model, store, namespace=embedding_model.model)
 index_path = "faiss_index"
 if os.path.exists(index_path):
-    vector_store = FAISS.load_local(index_path, embedding_model, allow_dangerous_deserialization=True)
+    vector_store = FAISS.load_local(index_path, cached_embedder, allow_dangerous_deserialization=True)
     print("Vector store loaded from saved index.")
 else:
-    vector_store = FAISS.from_documents(chunked_documents, embedding_model)
+    vector_store = FAISS.from_documents(chunked_documents, cached_embedder)
     print("Vector store created from documents.")
     vector_store.save_local(index_path)
     print("Vector store saved locally.")