diff --git a/docs/modules/models/llms/examples/human_input_llm.ipynb b/docs/modules/models/llms/examples/human_input_llm.ipynb
index 85593fe0..fc8ac630 100644
--- a/docs/modules/models/llms/examples/human_input_llm.ipynb
+++ b/docs/modules/models/llms/examples/human_input_llm.ipynb
@@ -5,7 +5,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# How (and why) to use the the human input LLM\n",
+    "# How (and why) to use the human input LLM\n",
     "\n",
     "Similar to the fake LLM, LangChain provides a pseudo LLM class that can be used for testing, debugging, or educational purposes. This allows you to mock out calls to the LLM and simulate how a human would respond if they received the prompts.\n",
     "\n",
@@ -34,6 +34,23 @@
     "from langchain.agents import AgentType"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since we will use the `WikipediaQueryRun` tool in this notebook, you might need to install the `wikipedia` package if you haven't done so already."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install wikipedia"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
@@ -217,7 +234,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.9"
   },
   "orig_nbformat": 4,
   "vscode": {