diff --git a/docs/modules/indexes/vectorstore_examples/chroma.ipynb b/docs/modules/indexes/vectorstore_examples/chroma.ipynb
index 3a793a4a..ce9610a4 100644
--- a/docs/modules/indexes/vectorstore_examples/chroma.ipynb
+++ b/docs/modules/indexes/vectorstore_examples/chroma.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "aac9563e",
    "metadata": {},
    "outputs": [],
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "a3c3999a",
    "metadata": {},
    "outputs": [],
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "5eabdb75",
    "metadata": {},
    "outputs": [
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "4b172de8",
    "metadata": {},
    "outputs": [
@@ -89,10 +89,115 @@
     "print(docs[0].page_content)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "8061454b",
+   "metadata": {},
+   "source": [
+    "## Persistance\n",
+    "\n",
+    "The below steps cover how to persist a ChromaDB instance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b76db26",
+   "metadata": {},
+   "source": [
+    "### Initialize PeristedChromaDB\n",
+    "Create embeddings for each chunk and insert into the Chroma vector database. The persist_directory argument tells ChromaDB where to store the database when it's persisted.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "cdb86e0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Chroma using direct local API.\n",
+      "No existing DB found in db, skipping load\n",
+      "No existing DB found in db, skipping load\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Embed and store the texts\n",
+    "# Supplying a persist_directory will store the embeddings on disk\n",
+    "persist_directory = 'db'\n",
+    "\n",
+    "embedding = OpenAIEmbeddings()\n",
+    "vectordb = Chroma.from_documents(documents=docs, embedding=embedding, persist_directory=persist_directory)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f568a322",
+   "metadata": {},
+   "source": [
+    "### Persist the Database\n",
+    "In a notebook, we should call persist() to ensure the embeddings are written to disk. This isn't necessary in a script - the database will be automatically persisted when the client object is destroyed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "74b08cb4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Persisting DB to disk, putting it in the save folder db\n",
+      "PersistentDuckDB del, about to run persist\n",
+      "Persisting DB to disk, putting it in the save folder db\n"
+     ]
+    }
+   ],
+   "source": [
+    "vectordb.persist()\n",
+    "vectordb = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc9ed900",
+   "metadata": {},
+   "source": [
+    "### Load the Database from disk, and create the chain\n",
+    "Be sure to pass the same persist_directory and embedding_function as you did when you instantiated the database. Initialize the chain we will use for question answering."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "31fecfe9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Chroma using direct local API.\n",
+      "loaded in 4 embeddings\n",
+      "loaded in 1 collections\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Now we can load the persisted database from disk, and use it as normal. \n",
+    "vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding)\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a359ed74",
+   "id": "4dde7a0d",
    "metadata": {},
    "outputs": [],
    "source": []