Update app.py

app.py

@@ -55,7 +55,7 @@ def init_chromadb():
 
     # Create the vectorestore to use as the index
     vectorstore = Chroma.from_documents(texts, embeddings, persist_directory=DB_DIR)
-    vectorstore.persist()
+    #vectorstore.persist()
     
     print(vectorstore)
     #vectorstore = None
@@ -79,7 +79,7 @@ def query_chromadb():
 #embeddings = OpenAIEmbeddings()
     # Load Vector store from local disk
     vectorstore = Chroma(persist_directory=DB_DIR, embedding_function=embeddings)
-    vectorstore.persist()
+    #vectorstore.persist()
 
     result = vectorstore.similarity_search_with_score(query="how to use AI in water conservation?", k=4)