diff --git a/lollms/app.py b/lollms/app.py
index 7c14ad1..cabc23f 100644
--- a/lollms/app.py
+++ b/lollms/app.py
@@ -921,7 +921,7 @@ class LollmsApplication(LoLLMsCom):
                 else:
                     query = current_message.content
                 try:
-                    docs, sorted_similarities, document_ids = self.personality.persona_data_vectorizer.recover_text(query, top_k=self.config.data_vectorization_nb_chunks)
+                    docs, sorted_similarities, document_ids = self.personality.persona_data_vectorizer.recover_text(query, top_k=int(self.config.data_vectorization_nb_chunks))
                     for doc, infos, doc_id in zip(docs, sorted_similarities, document_ids):
                         if self.config.data_vectorization_put_chunk_informations_into_context:
                             documentation += f"{start_header_id_template}document chunk{end_header_id_template}\nchunk_infos:{infos}\ncontent:{doc}\n"
@@ -959,7 +959,7 @@ class LollmsApplication(LoLLMsCom):
                         else:
                             documentation += f"{start_header_id_template}chunk{end_header_id_template}\n{content}\n"
 
-                    docs, sorted_similarities, document_ids = client.discussion.vectorizer.recover_text(query, top_k=self.config.data_vectorization_nb_chunks)
+                    docs, sorted_similarities, document_ids = client.discussion.vectorizer.recover_text(query, top_k=int(self.config.data_vectorization_nb_chunks))
                     for doc, infos in zip(docs, sorted_similarities):
                         if self.config.data_vectorization_force_first_chunk and len(client.discussion.vectorizer.chunks)>0 and infos[0]==doc_id:
                             continue
diff --git a/lollms/functions/knowledge/build_knowledge_db.py b/lollms/functions/knowledge/build_knowledge_db.py
index d609e4f..facb782 100644
--- a/lollms/functions/knowledge/build_knowledge_db.py
+++ b/lollms/functions/knowledge/build_knowledge_db.py
@@ -89,7 +89,7 @@ def buildKnowledgeDB(llm:APScript, data_store:TextVectorizer, data_folder_path:s
     qna_list=[]
     # Perform further processing with questions_vector
     for index, question in enumerate(questions_vector):
-        docs, sorted_similarities, document_ids = data_store.recover_text(question, top_k=llm.personality_config.data_vectorization_nb_chunks) 
+        docs, sorted_similarities, document_ids = data_store.recover_text(question, top_k=int(llm.personality_config.data_vectorization_nb_chunks)) 
         if llm.personality_config.use_enhanced_mode:
             llm.step_start(f"Verifying RAG data_{index}")
             prompt_text = """{llm.config.start_header_id_template}chunk: {{chunk}}