Update app.py

app.py

@@ -39,7 +39,7 @@ splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=10)
 docs = splitter.split_documents(docs)
 
 # Convert the document chunks to embedding and save them to the vector store
-vectorstore = Chroma.from_documents(docs, embedding=OpenAIEmbeddings(), persist_directory="./data")
+vectorstore = Chroma.from_documents(docs, embedding=OpenAIEmbeddings(), persist_directory="./db")
 vectorstore.persist()
 
 chain = ConversationalRetrievalChain.from_llm(