better model for the retriever: msmarco-distilbert-base-v4

app.py

@@ -41,7 +41,7 @@ model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32
 
 # sentence transformers to be used in vector store
 embeddings = HuggingFaceEmbeddings(
-      model_name="sentence-transformers/multi-qa-MiniLM-L6-cos-v1",
+      model_name="sentence-transformers/msmarco-distilbert-base-v4",
       model_kwargs={'device': 'cpu'},
       encode_kwargs={'normalize_embeddings': False}
   )
@@ -63,7 +63,7 @@ def prepare_vector_store_retriever(filename):
   documents = text_splitter.split_documents(raw_documents)
 
   # Creating a vectorstore
-  vectorstore = FAISS.from_documents(documents, embeddings, distance_strategy=DistanceStrategy.COSINE)
+  vectorstore = FAISS.from_documents(documents, embeddings, distance_strategy=DistanceStrategy.DOT_PRODUCT)
 
   return VectorStoreRetriever(vectorstore=vectorstore, search_kwargs={"k": 2})