diff --git a/examples/langchain-chroma/query.py b/examples/langchain-chroma/query.py
index 4ac662e0..2f7df507 100644
--- a/examples/langchain-chroma/query.py
+++ b/examples/langchain-chroma/query.py
@@ -2,25 +2,14 @@
 import os
 from langchain.vectorstores import Chroma
 from langchain.embeddings import OpenAIEmbeddings
-from langchain.text_splitter import RecursiveCharacterTextSplitter,CharacterTextSplitter
 from langchain.llms import OpenAI
 from langchain.chains import VectorDBQA
-from langchain.document_loaders import TextLoader
 
 base_path = os.environ.get('OPENAI_API_BASE', 'http://localhost:8080/v1')
 
 # Load and process the text
-loader = TextLoader('state_of_the_union.txt')
-documents = loader.load()
-
-text_splitter = CharacterTextSplitter(chunk_size=300, chunk_overlap=70)
-texts = text_splitter.split_documents(documents)
-
-# Embed and store the texts
-# Supplying a persist_directory will store the embeddings on disk
-persist_directory = 'db'
-
 embedding = OpenAIEmbeddings()
+persist_directory = 'db'
 
 # Now we can load the persisted database from disk, and use it as normal. 
 vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding)