Update app.py

app.py

@@ -108,19 +108,19 @@ def create_vector_database(loaded_documents):
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=30, length_function = len)
     chunked_documents = text_splitter.split_documents(loaded_documents)
 
-    # embeddings = HuggingFaceBgeEmbeddings(
-    #     model_name = "BAAI/bge-large-en"
-    # )
-    
-    model_name = "BAAI/bge-large-en"
-    model_kwargs = {'device': 'cpu'}
-    encode_kwargs = {'normalize_embeddings': False}
     embeddings = HuggingFaceBgeEmbeddings(
-    model_name=model_name,
-    model_kwargs=model_kwargs,
-    encode_kwargs=encode_kwargs
+        model_name = "BAAI/bge-large-en"
     )
     
+    # model_name = "BAAI/bge-large-en"
+    # model_kwargs = {'device': 'cpu'}
+    # encode_kwargs = {'normalize_embeddings': False}
+    # embeddings = HuggingFaceBgeEmbeddings(
+    # model_name=model_name,
+    # model_kwargs=model_kwargs,
+    # encode_kwargs=encode_kwargs
+    # )
+    
     # persist_directory = 'db'
     # Create and persist a Chroma vector database from the chunked documents
     db = Chroma.from_documents(