diff --git a/vector_store.py b/vector_store.py
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+++ b/vector_store.py
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+"""Vector store utilities for the FAQ bot.
+
+This module handles loading markdown files into a persistent Chroma vector store
+and provides a tool that performs semantic search over the stored documents.
+"""
+
+import os
+from pathlib import Path
+from typing import List
+
+from langchain_ollama import OllamaEmbeddings
+from langchain_chroma import Chroma
+from langchain_core.documents import Document
+
+from config import DATA_DIR, CHROMA_PERSIST_DIR, CHROMA_TOP_K
+from chunker import chunk_text
+
+# Initialize embeddings once
+embeddings = OllamaEmbeddings(model="nomic-embed-text")
+
+# Create or load the Chroma collection
+vector_store = Chroma(
+    collection_name="faq_collection",
+    embedding_function=embeddings,
+    persist_directory=CHROMA_PERSIST_DIR,
+)
+
+def load_faq_to_chroma() -> None:
+    """Load all markdown files from DATA_DIR into the Chroma vector store.
+
+    The function reads each .md file, splits it into chunks, creates Document
+    objects, and adds them to the vector store. The store is persisted to disk.
+    """
+    docs: List[Document] = []
+    for md_file in Path(DATA_DIR).glob("*.md"):
+        text = md_file.read_text(encoding="utf-8")
+        chunks = chunk_text(text)
+        for i, chunk in enumerate(chunks):
+            doc = Document(page_content=chunk, metadata={"source": md_file.name, "chunk_id": i})
+            docs.append(doc)
+    if docs:
+        vector_store.add_documents(docs)
+        vector_store.persist()
+
+def search_course_docs(query: str, k: int = CHROMA_TOP_K) -> str:
+    """Perform a semantic search over the FAQ collection.
+
+    Parameters
+    ----------
+    query: str
+        The user question.
+    k: int, optional
+        Number of top results to return.
+
+    Returns
+    -------
+    str
+        Concatenated top-k documents' page content.
+    """
+    results = vector_store.similarity_search_with_score(query, k=k)
+    # results is list of (Document, score)
+    snippets = [doc.page_content for doc, _ in results]
+    return "\n\n---\n\n".join(snippets)
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