diff --git a/docs/docs/modules/memory/types/vectorstore_retriever_memory.mdx b/docs/docs/modules/memory/types/vectorstore_retriever_memory.mdx
index 607f9180bb5..643d814fe1a 100644
--- a/docs/docs/modules/memory/types/vectorstore_retriever_memory.mdx
+++ b/docs/docs/modules/memory/types/vectorstore_retriever_memory.mdx
@@ -52,8 +52,6 @@ memory.save_context({"input": "I don't the Celtics"}, {"output": "ok"}) #
 
 
 ```python
-# Notice the first result returned is the memory pertaining to tax help, which the language model deems more semantically relevant
-# to a 1099 than the other documents, despite them both containing numbers.
 print(memory.load_memory_variables({"prompt": "what sport should i watch?"})["history"])
 ```
 
@@ -88,7 +86,6 @@ PROMPT = PromptTemplate(
 conversation_with_summary = ConversationChain(
     llm=llm,
     prompt=PROMPT,
-    # We set a very low max_token_limit for the purposes of testing.
     memory=memory,
     verbose=True
 )