diff --git a/examples/Question_answering_using_a_search_API.ipynb b/examples/Question_answering_using_a_search_API.ipynb
index e1190b5..2f02944 100644
--- a/examples/Question_answering_using_a_search_API.ipynb
+++ b/examples/Question_answering_using_a_search_API.ipynb
@@ -18,7 +18,7 @@
     "\n",
     "By combining these approaches, and drawing inspiration from [re-ranking](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) methods, we identify an approach that sits in the middle. **This approach can be implemented on top of any existing search system, like the Slack search API, or an internal ElasticSearch instance with private data**. Here’s how it works:\n",
     "\n",
-    "![search_augmented_by_query_generation_and_embeddings_reranking.png](../images/search_augmentation_embeddings.png)\n",
+    "![search_augmented_by_query_generation_and_embeddings_reranking.png](../images/search_rerank_answer.png)\n",
     "\n",
     "**Step 1: Search**\n",
     "\n",
@@ -93,7 +93,7 @@
     "\n",
     "def embeddings(input: list[str]) -> list[list[str]]:\n",
     "    response = openai.Embedding.create(model=\"text-embedding-ada-002\", input=input)\n",
-    "    return [data.embedding for data in response.data]\n"
+    "    return [data.embedding for data in response.data]"
    ]
   },
   {
@@ -113,7 +113,7 @@
    "outputs": [],
    "source": [
     "# User asks a question\n",
-    "USER_QUESTION = \"Who won the NBA championship? And who was the MVP? Tell me a bit about the last game.\"\n"
+    "USER_QUESTION = \"Who won the NBA championship? And who was the MVP? Tell me a bit about the last game.\""
    ]
   },
   {
@@ -179,7 +179,7 @@
     "# Let's include the original question as well for good measure\n",
     "queries.append(USER_QUESTION)\n",
     "\n",
-    "queries\n"
+    "queries"
    ]
   },
   {
@@ -282,7 +282,7 @@
     "    print(\"Title:\", article[\"title\"])\n",
     "    print(\"Description:\", article[\"description\"])\n",
     "    print(\"Content:\", article[\"content\"][0:100] + \"...\")\n",
-    "    print()"
+    "    print()\n"
    ]
   },
   {
@@ -326,7 +326,7 @@
     "\n",
     "hypothetical_answer = json_gpt(HA_INPUT)[\"hypotheticalAnswer\"]\n",
     "\n",
-    "hypothetical_answer"
+    "hypothetical_answer\n"
    ]
   },
   {
@@ -376,7 +376,7 @@
     "for article_embedding in article_embeddings:\n",
     "    cosine_similarities.append(dot(hypothetical_answer_embedding, article_embedding))\n",
     "\n",
-    "cosine_similarities[0:10]"
+    "cosine_similarities[0:10]\n"
    ]
   },
   {
@@ -440,7 +440,7 @@
     "    print(\"Description:\", article[\"description\"])\n",
     "    print(\"Content:\", article[\"content\"][0:100] + \"...\")\n",
     "    print(\"Score:\", score)\n",
-    "    print()"
+    "    print()\n"
    ]
   },
   {
@@ -500,7 +500,7 @@
     "for chunk in completion:\n",
     "    text += chunk.choices[0].delta.get(\"content\", \"\")\n",
     "    display.clear_output(wait=True)\n",
-    "    display.display(display.Markdown(text))\n"
+    "    display.display(display.Markdown(text))"
    ]
   }
  ],
diff --git a/images/search_augmentation_embeddings.png b/images/search_rerank_answer.png
similarity index 100%
rename from images/search_augmentation_embeddings.png
rename to images/search_rerank_answer.png