Updare retrive creation

seminar_edition_ai.py

@@ -166,6 +166,13 @@ def predictFromInit( sermonTopic, llmModelList = []):
   if answer != '':
     doc =  Document(page_content="text", metadata = {"source": "local"})
 
+    vectorstore = Chroma.from_documents(
+        documents=[doc],
+        embedding=embed_model,
+        persist_directory="chroma_db_dir_sermon",  # Local mode with in-memory storage only
+        collection_name="sermon_lab_ai"
+    )
+
     retriever = vectorstore.as_retriever(
         search_kwargs = {"k": 3}
     )