diff --git a/src/utils.py b/src/utils.py
index 9e3afb9..c1f0cb2 100644
--- a/src/utils.py
+++ b/src/utils.py
@@ -6,37 +6,23 @@ from langchain_text_splitters import RecursiveCharacterTextSplitter
 
 CHROMA_PATH = Path("./chroma_faq")
 
-
 def load_faq_to_chroma(md_dir: str = "data"):
-    """Load all .md files from md_dir into a persistent Chroma store.
-    The function will create or update the store at CHROMA_PATH.
-    """
-    # Ensure directory exists
     Path(md_dir).mkdir(parents=True, exist_ok=True)
-    # Gather all markdown files
     md_files = list(Path(md_dir).glob("*.md"))
     if not md_files:
         raise FileNotFoundError(f"No .md files found in {md_dir}")
-
-    # Read and split documents
     texts = []
     for md_file in md_files:
         text = md_file.read_text(encoding="utf-8")
         splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
         texts.extend(splitter.split_text(text))
-
-    # Create embeddings
     embeddings = OllamaEmbeddings(model="nomic-embed-text")
-
-    # Persist to Chroma
     chroma = Chroma(persist_directory=str(CHROMA_PATH), embedding_function=embeddings)
     chroma.add_texts(texts)
     chroma.persist()
     return chroma
 
-
 def search_course_docs(query: str, k: int = 3):
-    """Search the persistent Chroma store for the top k documents matching query."""
     chroma = Chroma(persist_directory=str(CHROMA_PATH), embedding_function=OllamaEmbeddings(model="nomic-embed-text"))
     retriever = chroma.as_retriever(search_kwargs={"k": k})
     return retriever.get_relevant_documents(query)