diff --git a/examples/Classification_using_embeddings.ipynb b/examples/Classification_using_embeddings.ipynb
index df62378..453aae4 100644
--- a/examples/Classification_using_embeddings.ipynb
+++ b/examples/Classification_using_embeddings.ipynb
@@ -23,14 +23,14 @@
      "text": [
       "              precision    recall  f1-score   support\n",
       "\n",
-      "           1       0.88      0.35      0.50        20\n",
+      "           1       0.89      0.40      0.55        20\n",
       "           2       1.00      0.38      0.55         8\n",
       "           3       1.00      0.18      0.31        11\n",
       "           4       1.00      0.26      0.41        27\n",
-      "           5       0.74      1.00      0.85       134\n",
+      "           5       0.75      1.00      0.86       134\n",
       "\n",
       "    accuracy                           0.77       200\n",
-      "   macro avg       0.92      0.43      0.52       200\n",
+      "   macro avg       0.93      0.44      0.53       200\n",
       "weighted avg       0.82      0.77      0.72       200\n",
       "\n"
      ]
@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -89,19 +89,17 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 648x720 with 1 Axes>"
+       "<Figure size 900x1000 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "from openai.embeddings_utils import plot_multiclass_precision_recall\n",
+    "from utils.embeddings_utils import plot_multiclass_precision_recall\n",
     "\n",
     "plot_multiclass_precision_recall(probas, y_test, [1, 2, 3, 4, 5], clf)\n"
    ]
@@ -131,7 +129,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.9"
+   "version": "3.9.16"
   },
   "orig_nbformat": 4,
   "vscode": {
diff --git a/examples/Get_embeddings_from_dataset.ipynb b/examples/Get_embeddings_from_dataset.ipynb
index 58612c5..d2e82e6 100644
--- a/examples/Get_embeddings_from_dataset.ipynb
+++ b/examples/Get_embeddings_from_dataset.ipynb
@@ -34,7 +34,7 @@
     "import pandas as pd\n",
     "import tiktoken\n",
     "\n",
-    "from openai.embeddings_utils import get_embedding\n"
+    "from utils.embeddings_utils import get_embedding\n"
    ]
   },
   {
@@ -212,7 +212,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.9 (main, Dec  7 2021, 18:04:56) \n[Clang 13.0.0 (clang-1300.0.29.3)]"
+   "version": "3.9.16"
   },
   "orig_nbformat": 4,
   "vscode": {
diff --git a/examples/Multiclass_classification_for_transactions.ipynb b/examples/Multiclass_classification_for_transactions.ipynb
index 7af8749..9f500c9 100644
--- a/examples/Multiclass_classification_for_transactions.ipynb
+++ b/examples/Multiclass_classification_for_transactions.ipynb
@@ -30,8 +30,8 @@
    "outputs": [],
    "source": [
     "%load_ext autoreload\n",
-    "%autoreload \n",
-    "%pip install openai 'openai[datalib]' 'openai[embeddings]' transformers"
+    "%autoreload\n",
+    "%pip install openai 'openai[datalib]' 'openai[embeddings]' transformers\n"
    ]
   },
   {
@@ -47,7 +47,7 @@
     "import os\n",
     "\n",
     "openai.api_key = os.getenv(\"OPENAI_API_KEY\")\n",
-    "COMPLETIONS_MODEL = \"text-davinci-002\""
+    "COMPLETIONS_MODEL = \"text-davinci-002\"\n"
    ]
   },
   {
@@ -85,7 +85,7 @@
    ],
    "source": [
     "transactions = pd.read_csv('./data/25000_spend_dataset_current.csv', encoding= 'unicode_escape')\n",
-    "len(transactions)"
+    "len(transactions)\n"
    ]
   },
   {
@@ -182,7 +182,7 @@
     }
    ],
    "source": [
-    "transactions.head()"
+    "transactions.head()\n"
    ]
   },
   {
@@ -192,7 +192,7 @@
    "outputs": [],
    "source": [
     "def request_completion(prompt):\n",
-    "    \n",
+    "\n",
     "    completion_response =   openai.Completion.create(\n",
     "                            prompt=prompt,\n",
     "                            temperature=0,\n",
@@ -202,17 +202,17 @@
     "                            presence_penalty=0,\n",
     "                            model=COMPLETIONS_MODEL\n",
     "                            )\n",
-    "        \n",
+    "\n",
     "    return completion_response\n",
     "\n",
     "def classify_transaction(transaction,prompt):\n",
-    "    \n",
+    "\n",
     "    prompt = prompt.replace('SUPPLIER_NAME',transaction['Supplier'])\n",
     "    prompt = prompt.replace('DESCRIPTION_TEXT',transaction['Description'])\n",
     "    prompt = prompt.replace('TRANSACTION_VALUE',str(transaction['Transaction value (£)']))\n",
-    "    \n",
+    "\n",
     "    classification = request_completion(prompt)['choices'][0]['text'].replace('\\n','')\n",
-    "    \n",
+    "\n",
     "    return classification\n",
     "\n",
     "# This function takes your training and validation outputs from the prepare_data function of the Finetuning API, and\n",
@@ -242,12 +242,12 @@
     "        valid_classes.add(result['completion'])\n",
     "        #print(f\"result: {result['completion']}\")\n",
     "        #print(isinstance(result, dict))\n",
-    "        \n",
+    "\n",
     "    if len(train_classes) == len(valid_classes):\n",
     "        print('All good')\n",
-    "        \n",
+    "\n",
     "    else:\n",
-    "        print('Classes do not match, please prepare data again')"
+    "        print('Classes do not match, please prepare data again')\n"
    ]
   },
   {
@@ -266,18 +266,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "zero_shot_prompt = '''You are a data expert working for the National Library of Scotland. \n",
+    "zero_shot_prompt = '''You are a data expert working for the National Library of Scotland.\n",
     "You are analysing all transactions over £25,000 in value and classifying them into one of five categories.\n",
     "The five categories are Building Improvement, Literature & Archive, Utility Bills, Professional Services and Software/IT.\n",
     "If you can't tell what it is, say Could not classify\n",
-    "                      \n",
+    "\n",
     "Transaction:\n",
-    "                      \n",
+    "\n",
     "Supplier: SUPPLIER_NAME\n",
     "Description: DESCRIPTION_TEXT\n",
     "Value: TRANSACTION_VALUE\n",
-    "                      \n",
-    "The classification is:'''"
+    "\n",
+    "The classification is:'''\n"
    ]
   },
   {
@@ -304,7 +304,7 @@
     "\n",
     "# Use our completion function to return a prediction\n",
     "completion_response = request_completion(prompt)\n",
-    "print(completion_response['choices'][0]['text'])"
+    "print(completion_response['choices'][0]['text'])\n"
    ]
   },
   {
@@ -337,7 +337,7 @@
    ],
    "source": [
     "test_transactions = transactions.iloc[:25]\n",
-    "test_transactions['Classification'] = test_transactions.apply(lambda x: classify_transaction(x,zero_shot_prompt),axis=1)"
+    "test_transactions['Classification'] = test_transactions.apply(lambda x: classify_transaction(x,zero_shot_prompt),axis=1)\n"
    ]
   },
   {
@@ -362,7 +362,7 @@
     }
    ],
    "source": [
-    "test_transactions['Classification'].value_counts()"
+    "test_transactions['Classification'].value_counts()\n"
    ]
   },
   {
@@ -665,7 +665,7 @@
     }
    ],
    "source": [
-    "test_transactions.head(25)"
+    "test_transactions.head(25)\n"
    ]
   },
   {
@@ -791,7 +791,7 @@
    ],
    "source": [
     "df = pd.read_csv('./data/labelled_transactions.csv')\n",
-    "df.head()"
+    "df.head()\n"
    ]
   },
   {
@@ -872,7 +872,7 @@
    ],
    "source": [
     "df['combined'] = \"Supplier: \" + df['Supplier'].str.strip() + \"; Description: \" + df['Description'].str.strip() + \"; Value: \" + str(df['Transaction value (£)']).strip()\n",
-    "df.head(2)"
+    "df.head(2)\n"
    ]
   },
   {
@@ -896,7 +896,7 @@
     "tokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")\n",
     "\n",
     "df['n_tokens'] = df.combined.apply(lambda x: len(tokenizer.encode(x)))\n",
-    "len(df)"
+    "len(df)\n"
    ]
   },
   {
@@ -905,7 +905,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "embedding_path = './data/transactions_with_embeddings_100.csv'"
+    "embedding_path = './data/transactions_with_embeddings_100.csv'\n"
    ]
   },
   {
@@ -914,11 +914,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from openai.embeddings_utils import get_embedding\n",
+    "from utils.embeddings_utils import get_embedding\n",
     "\n",
     "df['babbage_similarity'] = df.combined.apply(lambda x: get_embedding(x, engine='text-similarity-babbage-001'))\n",
     "df['babbage_search'] = df.combined.apply(lambda x: get_embedding(x, engine='text-search-babbage-doc-001'))\n",
-    "df.to_csv(embedding_path)"
+    "df.to_csv(embedding_path)\n"
    ]
   },
   {
@@ -1091,7 +1091,7 @@
     "\n",
     "fs_df = pd.read_csv(embedding_path)\n",
     "fs_df[\"babbage_similarity\"] = fs_df.babbage_similarity.apply(literal_eval).apply(np.array)\n",
-    "fs_df.head()"
+    "fs_df.head()\n"
    ]
   },
   {
@@ -1141,7 +1141,7 @@
     "probas = clf.predict_proba(X_test)\n",
     "\n",
     "report = classification_report(y_test, preds)\n",
-    "print(report)"
+    "print(report)\n"
    ]
   },
   {
@@ -1195,7 +1195,7 @@
    ],
    "source": [
     "ft_prep_df = fs_df.copy()\n",
-    "len(ft_prep_df)"
+    "len(ft_prep_df)\n"
    ]
   },
   {
@@ -1349,7 +1349,7 @@
     }
    ],
    "source": [
-    "ft_prep_df.head()"
+    "ft_prep_df.head()\n"
    ]
   },
   {
@@ -1378,7 +1378,7 @@
     "classes = list(set(ft_prep_df['Classification']))\n",
     "class_df = pd.DataFrame(classes).reset_index()\n",
     "class_df.columns = ['class_id','class']\n",
-    "class_df  , len(class_df)"
+    "class_df  , len(class_df)\n"
    ]
   },
   {
@@ -1559,7 +1559,7 @@
     "\n",
     "# Adding a common separator onto the end of each prompt so the model knows when a prompt is terminating\n",
     "ft_df_with_class['prompt'] = ft_df_with_class.apply(lambda x: x['combined'] + '\\n\\n###\\n\\n',axis=1)\n",
-    "ft_df_with_class.head()"
+    "ft_df_with_class.head()\n"
    ]
   },
   {
@@ -1647,7 +1647,7 @@
     "# In our case we don't, so we shuffle the data to give us a better chance of getting equal classes in our train and validation sets\n",
     "# Our fine-tuned model will error if we have less classes in the validation set, so this is a necessary step\n",
     "\n",
-    "import random \n",
+    "import random\n",
     "\n",
     "labels = [x for x in ft_df_with_class['class_id']]\n",
     "text = [x for x in ft_df_with_class['prompt']]\n",
@@ -1656,7 +1656,7 @@
     "ft_df['ordering'] = ft_df.apply(lambda x: random.randint(0,len(ft_df)), axis = 1)\n",
     "ft_df.set_index('ordering',inplace=True)\n",
     "ft_df_sorted = ft_df.sort_index(ascending=True)\n",
-    "ft_df_sorted.head()"
+    "ft_df_sorted.head()\n"
    ]
   },
   {
@@ -1670,7 +1670,7 @@
     "\n",
     "# We output our shuffled dataframe to a .jsonl file and run the prepare_data function to get us our input files\n",
     "ft_df_sorted.to_json(\"transactions_grouped.jsonl\", orient='records', lines=True)\n",
-    "!openai tools fine_tunes.prepare_data -f transactions_grouped.jsonl -q"
+    "!openai tools fine_tunes.prepare_data -f transactions_grouped.jsonl -q\n"
    ]
   },
   {
@@ -1691,7 +1691,7 @@
    "source": [
     "# This functions checks that your classes all appear in both prepared files\n",
     "# If they don't, the fine-tuned model creation will fail\n",
-    "check_finetune_classes('transactions_grouped_prepared_train.jsonl','transactions_grouped_prepared_valid.jsonl')"
+    "check_finetune_classes('transactions_grouped_prepared_train.jsonl','transactions_grouped_prepared_valid.jsonl')\n"
    ]
   },
   {
@@ -1704,7 +1704,7 @@
     "!openai api fine_tunes.create -t \"transactions_grouped_prepared_train.jsonl\" -v \"transactions_grouped_prepared_valid.jsonl\" --compute_classification_metrics --classification_n_classes 5 -m curie\n",
     "\n",
     "# You can use following command to get fine tuning job status and model name, replace the job name with your job\n",
-    "#!openai api fine_tunes.get -i ft-YBIc01t4hxYBC7I5qhRF3Qdx"
+    "#!openai api fine_tunes.get -i ft-YBIc01t4hxYBC7I5qhRF3Qdx\n"
    ]
   },
   {
@@ -1715,7 +1715,7 @@
    "source": [
     "# Congrats, you've got a fine-tuned model!\n",
     "# Copy/paste the name provided into the variable below and we'll take it for a spin\n",
-    "fine_tuned_model = 'curie:ft-personal-2022-10-20-10-42-56'"
+    "fine_tuned_model = 'curie:ft-personal-2022-10-20-10-42-56'\n"
    ]
   },
   {
@@ -1804,7 +1804,7 @@
    ],
    "source": [
     "test_set = pd.read_json('transactions_grouped_prepared_valid.jsonl', lines=True)\n",
-    "test_set.head()"
+    "test_set.head()\n"
    ]
   },
   {
@@ -1814,7 +1814,7 @@
    "outputs": [],
    "source": [
     "test_set['predicted_class'] = test_set.apply(lambda x: openai.Completion.create(model=fine_tuned_model, prompt=x['prompt'], max_tokens=1, temperature=0, logprobs=5),axis=1)\n",
-    "test_set['pred'] = test_set.apply(lambda x : x['predicted_class']['choices'][0]['text'],axis=1)"
+    "test_set['pred'] = test_set.apply(lambda x : x['predicted_class']['choices'][0]['text'],axis=1)\n"
    ]
   },
   {
@@ -1823,7 +1823,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "test_set['result'] = test_set.apply(lambda x: str(x['pred']).strip() == str(x['completion']).strip(), axis = 1)"
+    "test_set['result'] = test_set.apply(lambda x: str(x['pred']).strip() == str(x['completion']).strip(), axis = 1)\n"
    ]
   },
   {
@@ -1845,7 +1845,7 @@
     }
    ],
    "source": [
-    "test_set['result'].value_counts()"
+    "test_set['result'].value_counts()\n"
    ]
   },
   {
@@ -1953,7 +1953,7 @@
    ],
    "source": [
     "holdout_df = transactions.copy().iloc[101:]\n",
-    "holdout_df.head()"
+    "holdout_df.head()\n"
    ]
   },
   {
@@ -1964,7 +1964,7 @@
    "source": [
     "holdout_df['combined'] = \"Supplier: \" + holdout_df['Supplier'].str.strip() + \"; Description: \" + holdout_df['Description'].str.strip() + '\\n\\n###\\n\\n' # + \"; Value: \" + str(df['Transaction value (£)']).strip()\n",
     "holdout_df['prediction_result'] = holdout_df.apply(lambda x: openai.Completion.create(model=fine_tuned_model, prompt=x['combined'], max_tokens=1, temperature=0, logprobs=5),axis=1)\n",
-    "holdout_df['pred'] = holdout_df.apply(lambda x : x['prediction_result']['choices'][0]['text'],axis=1)"
+    "holdout_df['pred'] = holdout_df.apply(lambda x : x['prediction_result']['choices'][0]['text'],axis=1)\n"
    ]
   },
   {
@@ -2151,7 +2151,7 @@
     }
    ],
    "source": [
-    "holdout_df.head(10)"
+    "holdout_df.head(10)\n"
    ]
   },
   {
@@ -2173,7 +2173,7 @@
     }
    ],
    "source": [
-    "holdout_df['pred'].value_counts()"
+    "holdout_df['pred'].value_counts()\n"
    ]
   },
   {
diff --git a/examples/Recommendation_using_embeddings.ipynb b/examples/Recommendation_using_embeddings.ipynb
index 19036a6..0a7c3dc 100644
--- a/examples/Recommendation_using_embeddings.ipynb
+++ b/examples/Recommendation_using_embeddings.ipynb
@@ -37,7 +37,7 @@
     "import pandas as pd\n",
     "import pickle\n",
     "\n",
-    "from openai.embeddings_utils import (\n",
+    "from utils.embeddings_utils import (\n",
     "    get_embedding,\n",
     "    distances_from_embeddings,\n",
     "    tsne_components_from_embeddings,\n",
@@ -46,7 +46,7 @@
     ")\n",
     "\n",
     "# constants\n",
-    "EMBEDDING_MODEL = \"text-embedding-ada-002\""
+    "EMBEDDING_MODEL = \"text-embedding-ada-002\"\n"
    ]
   },
   {
@@ -158,7 +158,7 @@
     "\n",
     "# print dataframe\n",
     "n_examples = 5\n",
-    "df.head(n_examples)"
+    "df.head(n_examples)\n"
    ]
   },
   {
@@ -206,7 +206,7 @@
     "    print(\"\")\n",
     "    print(f\"Title: {row['title']}\")\n",
     "    print(f\"Description: {row['description']}\")\n",
-    "    print(f\"Label: {row['label']}\")"
+    "    print(f\"Label: {row['label']}\")\n"
    ]
   },
   {
@@ -252,7 +252,7 @@
     "        embedding_cache[(string, model)] = get_embedding(string, model)\n",
     "        with open(embedding_cache_path, \"wb\") as embedding_cache_file:\n",
     "            pickle.dump(embedding_cache, embedding_cache_file)\n",
-    "    return embedding_cache[(string, model)]"
+    "    return embedding_cache[(string, model)]\n"
    ]
   },
   {
@@ -285,7 +285,7 @@
     "\n",
     "# print the first 10 dimensions of the embedding\n",
     "example_embedding = embedding_from_string(example_string)\n",
-    "print(f\"\\nExample embedding: {example_embedding[:10]}...\")"
+    "print(f\"\\nExample embedding: {example_embedding[:10]}...\")\n"
    ]
   },
   {
@@ -317,9 +317,9 @@
     "    embeddings = [embedding_from_string(string, model=model) for string in strings]\n",
     "    # get the embedding of the source string\n",
     "    query_embedding = embeddings[index_of_source_string]\n",
-    "    # get distances between the source embedding and other embeddings (function from embeddings_utils.py)\n",
+    "    # get distances between the source embedding and other embeddings (function from utils.embeddings_utils.py)\n",
     "    distances = distances_from_embeddings(query_embedding, embeddings, distance_metric=\"cosine\")\n",
-    "    # get indices of nearest neighbors (function from embeddings_utils.py)\n",
+    "    # get indices of nearest neighbors (function from utils.utils.embeddings_utils.py)\n",
     "    indices_of_nearest_neighbors = indices_of_nearest_neighbors_from_distances(distances)\n",
     "\n",
     "    # print out source string\n",
@@ -344,7 +344,7 @@
     "        Distance: {distances[i]:0.3f}\"\"\"\n",
     "        )\n",
     "\n",
-    "    return indices_of_nearest_neighbors"
+    "    return indices_of_nearest_neighbors\n"
    ]
   },
   {
@@ -396,7 +396,7 @@
     "    strings=article_descriptions,  # let's base similarity off of the article description\n",
     "    index_of_source_string=0,  # let's look at articles similar to the first one about Tony Blair\n",
     "    k_nearest_neighbors=5,  # let's look at the 5 most similar articles\n",
-    ")"
+    ")\n"
    ]
   },
   {
@@ -452,7 +452,7 @@
     "    strings=article_descriptions,  # let's base similarity off of the article description\n",
     "    index_of_source_string=1,  # let's look at articles similar to the second one about a more secure chipset\n",
     "    k_nearest_neighbors=5,  # let's look at the 5 most similar articles\n",
-    ")"
+    ")\n"
    ]
   },
   {
@@ -11456,7 +11456,7 @@
     "    width=600,\n",
     "    height=500,\n",
     "    title=\"t-SNE components of article descriptions\",\n",
-    ")"
+    ")\n"
    ]
   },
   {
@@ -11498,7 +11498,7 @@
     "\n",
     "tony_blair_labels = nearest_neighbor_labels(tony_blair_articles, k_nearest_neighbors=5)\n",
     "chipset_security_labels = nearest_neighbor_labels(chipset_security_articles, k_nearest_neighbors=5\n",
-    ")"
+    ")\n"
    ]
   },
   {
@@ -22443,7 +22443,7 @@
     "    height=500,\n",
     "    title=\"Nearest neighbors of the Tony Blair article\",\n",
     "    category_orders={\"label\": [\"Other\", \"Nearest neighbor (top 5)\", \"Source\"]},\n",
-    ")"
+    ")\n"
    ]
   },
   {
@@ -33396,7 +33396,7 @@
     "    height=500,\n",
     "    title=\"Nearest neighbors of the chipset security article\",\n",
     "    category_orders={\"label\": [\"Other\", \"Nearest neighbor (top 5)\", \"Source\"]},\n",
-    ")"
+    ")\n"
    ]
   },
   {
diff --git a/examples/Semantic_text_search_using_embeddings.ipynb b/examples/Semantic_text_search_using_embeddings.ipynb
index 21d032e..f6e59ff 100644
--- a/examples/Semantic_text_search_using_embeddings.ipynb
+++ b/examples/Semantic_text_search_using_embeddings.ipynb
@@ -53,7 +53,7 @@
     }
    ],
    "source": [
-    "from openai.embeddings_utils import get_embedding, cosine_similarity\n",
+    "from utils.embeddings_utils import get_embedding, cosine_similarity\n",
     "\n",
     "# search through the reviews for a specific product\n",
     "def search_reviews(df, product_description, n=3, pprint=True):\n",
@@ -98,7 +98,7 @@
     }
    ],
    "source": [
-    "results = search_reviews(df, \"whole wheat pasta\", n=3)"
+    "results = search_reviews(df, \"whole wheat pasta\", n=3)\n"
    ]
   },
   {
@@ -124,7 +124,7 @@
     }
    ],
    "source": [
-    "results = search_reviews(df, \"bad delivery\", n=1)"
+    "results = search_reviews(df, \"bad delivery\", n=1)\n"
    ]
   },
   {
@@ -150,7 +150,7 @@
     }
    ],
    "source": [
-    "results = search_reviews(df, \"spoilt\", n=1)"
+    "results = search_reviews(df, \"spoilt\", n=1)\n"
    ]
   },
   {
@@ -170,7 +170,7 @@
     }
    ],
    "source": [
-    "results = search_reviews(df, \"pet food\", n=2)"
+    "results = search_reviews(df, \"pet food\", n=2)\n"
    ]
   }
  ],
diff --git a/examples/User_and_product_embeddings.ipynb b/examples/User_and_product_embeddings.ipynb
index a05c4e5..a0249ba 100644
--- a/examples/User_and_product_embeddings.ipynb
+++ b/examples/User_and_product_embeddings.ipynb
@@ -75,7 +75,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from openai.embeddings_utils import cosine_similarity\n",
+    "from utils.embeddings_utils import cosine_similarity\n",
     "\n",
     "# evaluate embeddings as recommendations on X_test\n",
     "def evaluate_single_match(row):\n",
@@ -140,7 +140,7 @@
     "X_test.boxplot(column='percentile_cosine_similarity', by='Score')\n",
     "plt.title('')\n",
     "plt.show()\n",
-    "plt.close()"
+    "plt.close()\n"
    ]
   },
   {
diff --git a/examples/Visualizing_embeddings_in_3D.ipynb b/examples/Visualizing_embeddings_in_3D.ipynb
index 92e081d..d0b923c 100644
--- a/examples/Visualizing_embeddings_in_3D.ipynb
+++ b/examples/Visualizing_embeddings_in_3D.ipynb
@@ -126,7 +126,7 @@
     "samples = pd.read_json(\"data/dbpedia_samples.jsonl\", lines=True)\n",
     "categories = sorted(samples[\"category\"].unique())\n",
     "print(\"Categories of DBpedia samples:\", samples[\"category\"].value_counts())\n",
-    "samples.head()"
+    "samples.head()\n"
    ]
   },
   {
@@ -136,9 +136,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from openai.embeddings_utils import get_embeddings\n",
+    "from utils.embeddings_utils import get_embeddings\n",
     "# NOTE: The following code will send a query of batch size 200 to /embeddings\n",
-    "matrix = get_embeddings(samples[\"text\"].to_list(), engine=\"text-embedding-ada-002\")"
+    "matrix = get_embeddings(samples[\"text\"].to_list(), engine=\"text-embedding-ada-002\")\n"
    ]
   },
   {
@@ -159,7 +159,7 @@
     "from sklearn.decomposition import PCA\n",
     "pca = PCA(n_components=3)\n",
     "vis_dims = pca.fit_transform(matrix)\n",
-    "samples[\"embed_vis\"] = vis_dims.tolist()"
+    "samples[\"embed_vis\"] = vis_dims.tolist()\n"
    ]
   },
   {
@@ -233,7 +233,7 @@
     "ax.set_xlabel('x')\n",
     "ax.set_ylabel('y')\n",
     "ax.set_zlabel('z')\n",
-    "ax.legend(bbox_to_anchor=(1.1, 1))"
+    "ax.legend(bbox_to_anchor=(1.1, 1))\n"
    ]
   }
  ],
diff --git a/examples/Zero-shot_classification_with_embeddings.ipynb b/examples/Zero-shot_classification_with_embeddings.ipynb
index 1c87c20..7bb331c 100644
--- a/examples/Zero-shot_classification_with_embeddings.ipynb
+++ b/examples/Zero-shot_classification_with_embeddings.ipynb
@@ -86,11 +86,11 @@
     }
    ],
    "source": [
-    "from openai.embeddings_utils import cosine_similarity, get_embedding\n",
+    "from utils.embeddings_utils import cosine_similarity, get_embedding\n",
     "from sklearn.metrics import PrecisionRecallDisplay\n",
     "\n",
     "def evaluate_embeddings_approach(\n",
-    "    labels = ['negative', 'positive'], \n",
+    "    labels = ['negative', 'positive'],\n",
     "    model = EMBEDDING_MODEL,\n",
     "):\n",
     "    label_embeddings = [get_embedding(label, engine=model) for label in labels]\n",
@@ -107,7 +107,7 @@
     "    display = PrecisionRecallDisplay.from_predictions(df.sentiment, probas, pos_label='positive')\n",
     "    _ = display.ax_.set_title(\"2-class Precision-Recall curve\")\n",
     "\n",
-    "evaluate_embeddings_approach(labels=['negative', 'positive'], model=EMBEDDING_MODEL)"
+    "evaluate_embeddings_approach(labels=['negative', 'positive'], model=EMBEDDING_MODEL)\n"
    ]
   },
   {
@@ -152,7 +152,7 @@
     }
    ],
    "source": [
-    "evaluate_embeddings_approach(labels=['An Amazon review with a negative sentiment.', 'An Amazon review with a positive sentiment.'])"
+    "evaluate_embeddings_approach(labels=['An Amazon review with a negative sentiment.', 'An Amazon review with a positive sentiment.'])\n"
    ]
   },
   {
@@ -197,7 +197,7 @@
     }
    ],
    "source": [
-    "evaluate_embeddings_approach(labels=['An Amazon review with a negative sentiment.', 'An Amazon review with a positive sentiment.'])"
+    "evaluate_embeddings_approach(labels=['An Amazon review with a negative sentiment.', 'An Amazon review with a positive sentiment.'])\n"
    ]
   },
   {
diff --git a/examples/utils/embeddings_utils.py b/examples/utils/embeddings_utils.py
new file mode 100644
index 0000000..efb306d
--- /dev/null
+++ b/examples/utils/embeddings_utils.py
@@ -0,0 +1,252 @@
+import textwrap as tr
+from typing import List, Optional
+
+import matplotlib.pyplot as plt
+import plotly.express as px
+from scipy import spatial
+from sklearn.decomposition import PCA
+from sklearn.manifold import TSNE
+from sklearn.metrics import average_precision_score, precision_recall_curve
+from tenacity import retry, stop_after_attempt, wait_random_exponential
+
+import openai
+import numpy as np
+import pandas as pd
+
+
+@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))
+def get_embedding(text: str, engine="text-similarity-davinci-001", **kwargs) -> List[float]:
+
+    # replace newlines, which can negatively affect performance.
+    text = text.replace("\n", " ")
+
+    return openai.Embedding.create(input=[text], engine=engine, **kwargs)["data"][0]["embedding"]
+
+
+@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))
+async def aget_embedding(
+    text: str, engine="text-similarity-davinci-001", **kwargs
+) -> List[float]:
+
+    # replace newlines, which can negatively affect performance.
+    text = text.replace("\n", " ")
+
+    return (await openai.Embedding.acreate(input=[text], engine=engine, **kwargs))["data"][0][
+        "embedding"
+    ]
+
+
+@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))
+def get_embeddings(
+    list_of_text: List[str], engine="text-similarity-babbage-001", **kwargs
+) -> List[List[float]]:
+    assert len(list_of_text) <= 2048, "The batch size should not be larger than 2048."
+
+    # replace newlines, which can negatively affect performance.
+    list_of_text = [text.replace("\n", " ") for text in list_of_text]
+
+    data = openai.Embedding.create(input=list_of_text, engine=engine, **kwargs).data
+    return [d["embedding"] for d in data]
+
+
+@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))
+async def aget_embeddings(
+    list_of_text: List[str], engine="text-similarity-babbage-001", **kwargs
+) -> List[List[float]]:
+    assert len(list_of_text) <= 2048, "The batch size should not be larger than 2048."
+
+    # replace newlines, which can negatively affect performance.
+    list_of_text = [text.replace("\n", " ") for text in list_of_text]
+
+    data = (await openai.Embedding.acreate(input=list_of_text, engine=engine, **kwargs)).data
+    return [d["embedding"] for d in data]
+
+
+def cosine_similarity(a, b):
+    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
+
+
+def plot_multiclass_precision_recall(
+    y_score, y_true_untransformed, class_list, classifier_name
+):
+    """
+    Precision-Recall plotting for a multiclass problem. It plots average precision-recall, per class precision recall and reference f1 contours.
+
+    Code slightly modified, but heavily based on https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html
+    """
+    n_classes = len(class_list)
+    y_true = pd.concat(
+        [(y_true_untransformed == class_list[i]) for i in range(n_classes)], axis=1
+    ).values
+
+    # For each class
+    precision = dict()
+    recall = dict()
+    average_precision = dict()
+    for i in range(n_classes):
+        precision[i], recall[i], _ = precision_recall_curve(y_true[:, i], y_score[:, i])
+        average_precision[i] = average_precision_score(y_true[:, i], y_score[:, i])
+
+    # A "micro-average": quantifying score on all classes jointly
+    precision_micro, recall_micro, _ = precision_recall_curve(
+        y_true.ravel(), y_score.ravel()
+    )
+    average_precision_micro = average_precision_score(y_true, y_score, average="micro")
+    print(
+        str(classifier_name)
+        + " - Average precision score over all classes: {0:0.2f}".format(
+            average_precision_micro
+        )
+    )
+
+    # setup plot details
+    plt.figure(figsize=(9, 10))
+    f_scores = np.linspace(0.2, 0.8, num=4)
+    lines = []
+    labels = []
+    for f_score in f_scores:
+        x = np.linspace(0.01, 1)
+        y = f_score * x / (2 * x - f_score)
+        (l,) = plt.plot(x[y >= 0], y[y >= 0], color="gray", alpha=0.2)
+        plt.annotate("f1={0:0.1f}".format(f_score), xy=(0.9, y[45] + 0.02))
+
+    lines.append(l)
+    labels.append("iso-f1 curves")
+    (l,) = plt.plot(recall_micro, precision_micro, color="gold", lw=2)
+    lines.append(l)
+    labels.append(
+        "average Precision-recall (auprc = {0:0.2f})" "".format(average_precision_micro)
+    )
+
+    for i in range(n_classes):
+        (l,) = plt.plot(recall[i], precision[i], lw=2)
+        lines.append(l)
+        labels.append(
+            "Precision-recall for class `{0}` (auprc = {1:0.2f})"
+            "".format(class_list[i], average_precision[i])
+        )
+
+    fig = plt.gcf()
+    fig.subplots_adjust(bottom=0.25)
+    plt.xlim([0.0, 1.0])
+    plt.ylim([0.0, 1.05])
+    plt.xlabel("Recall")
+    plt.ylabel("Precision")
+    plt.title(f"{classifier_name}: Precision-Recall curve for each class")
+    plt.legend(lines, labels)
+
+
+def distances_from_embeddings(
+    query_embedding: List[float],
+    embeddings: List[List[float]],
+    distance_metric="cosine",
+) -> List[List]:
+    """Return the distances between a query embedding and a list of embeddings."""
+    distance_metrics = {
+        "cosine": spatial.distance.cosine,
+        "L1": spatial.distance.cityblock,
+        "L2": spatial.distance.euclidean,
+        "Linf": spatial.distance.chebyshev,
+    }
+    distances = [
+        distance_metrics[distance_metric](query_embedding, embedding)
+        for embedding in embeddings
+    ]
+    return distances
+
+
+def indices_of_nearest_neighbors_from_distances(distances) -> np.ndarray:
+    """Return a list of indices of nearest neighbors from a list of distances."""
+    return np.argsort(distances)
+
+
+def pca_components_from_embeddings(
+    embeddings: List[List[float]], n_components=2
+) -> np.ndarray:
+    """Return the PCA components of a list of embeddings."""
+    pca = PCA(n_components=n_components)
+    array_of_embeddings = np.array(embeddings)
+    return pca.fit_transform(array_of_embeddings)
+
+
+def tsne_components_from_embeddings(
+    embeddings: List[List[float]], n_components=2, **kwargs
+) -> np.ndarray:
+    """Returns t-SNE components of a list of embeddings."""
+    # use better defaults if not specified
+    if "init" not in kwargs.keys():
+        kwargs["init"] = "pca"
+    if "learning_rate" not in kwargs.keys():
+        kwargs["learning_rate"] = "auto"
+    tsne = TSNE(n_components=n_components, **kwargs)
+    array_of_embeddings = np.array(embeddings)
+    return tsne.fit_transform(array_of_embeddings)
+
+
+def chart_from_components(
+    components: np.ndarray,
+    labels: Optional[List[str]] = None,
+    strings: Optional[List[str]] = None,
+    x_title="Component 0",
+    y_title="Component 1",
+    mark_size=5,
+    **kwargs,
+):
+    """Return an interactive 2D chart of embedding components."""
+    empty_list = ["" for _ in components]
+    data = pd.DataFrame(
+        {
+            x_title: components[:, 0],
+            y_title: components[:, 1],
+            "label": labels if labels else empty_list,
+            "string": ["<br>".join(tr.wrap(string, width=30)) for string in strings]
+            if strings
+            else empty_list,
+        }
+    )
+    chart = px.scatter(
+        data,
+        x=x_title,
+        y=y_title,
+        color="label" if labels else None,
+        symbol="label" if labels else None,
+        hover_data=["string"] if strings else None,
+        **kwargs,
+    ).update_traces(marker=dict(size=mark_size))
+    return chart
+
+
+def chart_from_components_3D(
+    components: np.ndarray,
+    labels: Optional[List[str]] = None,
+    strings: Optional[List[str]] = None,
+    x_title: str = "Component 0",
+    y_title: str = "Component 1",
+    z_title: str = "Compontent 2",
+    mark_size: int = 5,
+    **kwargs,
+):
+    """Return an interactive 3D chart of embedding components."""
+    empty_list = ["" for _ in components]
+    data = pd.DataFrame(
+        {
+            x_title: components[:, 0],
+            y_title: components[:, 1],
+            z_title: components[:, 2],
+            "label": labels if labels else empty_list,
+            "string": ["<br>".join(tr.wrap(string, width=30)) for string in strings]
+            if strings
+            else empty_list,
+        }
+    )
+    chart = px.scatter_3d(
+        data,
+        x=x_title,
+        y=y_title,
+        z=z_title,
+        color="label" if labels else None,
+        symbol="label" if labels else None,
+        hover_data=["string"] if strings else None,
+        **kwargs,
+    ).update_traces(marker=dict(size=mark_size))
+    return chart
diff --git a/examples/vector_databases/SingleStoreDB/OpenAI_wikipedia_semantic_search.ipynb b/examples/vector_databases/SingleStoreDB/OpenAI_wikipedia_semantic_search.ipynb
index cba337c..ec2e1a5 100644
--- a/examples/vector_databases/SingleStoreDB/OpenAI_wikipedia_semantic_search.ipynb
+++ b/examples/vector_databases/SingleStoreDB/OpenAI_wikipedia_semantic_search.ipynb
@@ -34,7 +34,7 @@
     }
    ],
    "source": [
-    "!pip install openai --quiet"
+    "!pip install openai --quiet\n"
    ]
   },
   {
@@ -48,7 +48,7 @@
     "\n",
     "# models\n",
     "EMBEDDING_MODEL = \"text-embedding-ada-002\"\n",
-    "GPT_MODEL = \"gpt-3.5-turbo\""
+    "GPT_MODEL = \"gpt-3.5-turbo\"\n"
    ]
   },
   {
@@ -84,7 +84,7 @@
     "    ]\n",
     ")\n",
     "\n",
-    "print(response['choices'][0]['message']['content'])"
+    "print(response['choices'][0]['message']['content'])\n"
    ]
   },
   {
@@ -120,7 +120,7 @@
     }
    ],
    "source": [
-    "!pip install matplotlib plotly.express scikit-learn tabulate tiktoken wget --quiet"
+    "!pip install matplotlib plotly.express scikit-learn tabulate tiktoken wget --quiet\n"
    ]
   },
   {
@@ -133,7 +133,7 @@
     "import pandas as pd\n",
     "import os\n",
     "import wget\n",
-    "import ast"
+    "import ast\n"
    ]
   },
   {
@@ -168,7 +168,7 @@
     "    wget.download(embeddings_path, file_path)\n",
     "    print(\"File downloaded successfully.\")\n",
     "else:\n",
-    "    print(\"File already exists in the local file system.\")"
+    "    print(\"File already exists in the local file system.\")\n"
    ]
   },
   {
@@ -183,7 +183,7 @@
     ")\n",
     "\n",
     "# convert embeddings from CSV str type back to list type\n",
-    "df['embedding'] = df['embedding'].apply(ast.literal_eval)"
+    "df['embedding'] = df['embedding'].apply(ast.literal_eval)\n"
    ]
   },
   {
@@ -193,7 +193,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df"
+    "df\n"
    ]
   },
   {
@@ -219,7 +219,7 @@
     }
    ],
    "source": [
-    "df.info(show_counts=True)"
+    "df.info(show_counts=True)\n"
    ]
   },
   {
@@ -241,7 +241,7 @@
     "\n",
     "conn = s2.connect(\"<user>:<Password>@<host>:3306/\")\n",
     "\n",
-    "cur = conn.cursor()"
+    "cur = conn.cursor()\n"
    ]
   },
   {
@@ -267,7 +267,7 @@
     "    CREATE DATABASE IF NOT EXISTS winter_wikipedia2;\n",
     "\"\"\"\n",
     "\n",
-    "cur.execute(stmt)"
+    "cur.execute(stmt)\n"
    ]
   },
   {
@@ -296,7 +296,7 @@
     "    embedding BLOB\n",
     ");\"\"\"\n",
     "\n",
-    "cur.execute(stmt)"
+    "cur.execute(stmt)\n"
    ]
   },
   {
@@ -349,7 +349,7 @@
     "for i in range(0, len(record_arr), batch_size):\n",
     "    batch = record_arr[i:i+batch_size]\n",
     "    values = [(row[0], row[1], str(row[2])) for row in batch]\n",
-    "    cur.executemany(stmt, values)"
+    "    cur.executemany(stmt, values)\n"
    ]
   },
   {
@@ -367,7 +367,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from openai.embeddings_utils import get_embedding\n",
+    "from utils.embeddings_utils import get_embedding\n",
     "\n",
     "def strings_ranked_by_relatedness(\n",
     "    query: str,\n",
@@ -395,7 +395,7 @@
     "\n",
     "    # Fetch the results\n",
     "    results = cur.fetchall()\n",
-    "    \n",
+    "\n",
     "    strings = []\n",
     "    relatednesses = []\n",
     "\n",
@@ -404,7 +404,7 @@
     "        relatednesses.append(row[1])\n",
     "\n",
     "    # Return the results.\n",
-    "    return strings[:top_n], relatednesses[:top_n]"
+    "    return strings[:top_n], relatednesses[:top_n]\n"
    ]
   },
   {
@@ -424,7 +424,7 @@
     "\n",
     "for string, relatedness in zip(strings, relatednesses):\n",
     "    print(f\"{relatedness=:.3f}\")\n",
-    "    print(tabulate([[string]], headers=['Result'], tablefmt='fancy_grid'))"
+    "    print(tabulate([[string]], headers=['Result'], tablefmt='fancy_grid'))\n"
    ]
   },
   {
@@ -494,7 +494,7 @@
     "        temperature=0\n",
     "    )\n",
     "    response_message = response[\"choices\"][0][\"message\"][\"content\"]\n",
-    "    return response_message"
+    "    return response_message\n"
    ]
   },
   {
@@ -531,7 +531,7 @@
     "\n",
     "answer = ask('Who won the gold medal for curling in Olymics 2022?')\n",
     "\n",
-    "pprint(answer)"
+    "pprint(answer)\n"
    ]
   }
  ],
diff --git a/examples/vector_databases/redis/redis-hybrid-query-examples.ipynb b/examples/vector_databases/redis/redis-hybrid-query-examples.ipynb
index e24a68f..d2cf0d6 100644
--- a/examples/vector_databases/redis/redis-hybrid-query-examples.ipynb
+++ b/examples/vector_databases/redis/redis-hybrid-query-examples.ipynb
@@ -87,7 +87,7 @@
     }
    ],
    "source": [
-    "! pip install redis pandas openai"
+    "! pip install redis pandas openai\n"
    ]
   },
   {
@@ -303,7 +303,7 @@
     "import numpy as np\n",
     "from typing import List\n",
     "\n",
-    "from openai.embeddings_utils import (\n",
+    "from utils.embeddings_utils import (\n",
     "    get_embeddings,\n",
     "    distances_from_embeddings,\n",
     "    tsne_components_from_embeddings,\n",
@@ -322,7 +322,7 @@
     "\n",
     "# print dataframe\n",
     "n_examples = 5\n",
-    "df.head(n_examples)"
+    "df.head(n_examples)\n"
    ]
   },
   {
@@ -360,7 +360,7 @@
     "df[\"product_text\"] = df.apply(lambda row: f\"name {row['productDisplayName']} category {row['masterCategory']} subcategory {row['subCategory']} color {row['baseColour']} gender {row['gender']}\".lower(), axis=1)\n",
     "df.rename({\"id\":\"product_id\"}, inplace=True, axis=1)\n",
     "\n",
-    "df.info()"
+    "df.info()\n"
    ]
   },
   {
@@ -382,7 +382,7 @@
    ],
    "source": [
     "# check out one of the texts we will use to create semantic embeddings\n",
-    "df[\"product_text\"][0]"
+    "df[\"product_text\"][0]\n"
    ]
   },
   {
@@ -437,7 +437,7 @@
     "    port=REDIS_PORT,\n",
     "    password=REDIS_PASSWORD\n",
     ")\n",
-    "redis_client.ping()"
+    "redis_client.ping()\n"
    ]
   },
   {
@@ -467,7 +467,7 @@
     "INDEX_NAME = \"product_embeddings\"           # name of the search index\n",
     "PREFIX = \"doc\"                            # prefix for the document keys\n",
     "DISTANCE_METRIC = \"L2\"                # distance metric for the vectors (ex. COSINE, IP, L2)\n",
-    "NUMBER_OF_VECTORS = len(df)"
+    "NUMBER_OF_VECTORS = len(df)\n"
    ]
   },
   {
@@ -492,7 +492,7 @@
     "        \"INITIAL_CAP\": NUMBER_OF_VECTORS,\n",
     "    }\n",
     ")\n",
-    "fields = [name, category, articleType, gender, season, year, text_embedding]"
+    "fields = [name, category, articleType, gender, season, year, text_embedding]\n"
    ]
   },
   {
@@ -511,7 +511,7 @@
     "    redis_client.ft(INDEX_NAME).create_index(\n",
     "        fields = fields,\n",
     "        definition = IndexDefinition(prefix=[PREFIX], index_type=IndexType.HASH)\n",
-    ")"
+    ")\n"
    ]
   },
   {
@@ -538,7 +538,7 @@
     "    product_vectors = []\n",
     "    docs = []\n",
     "    batchsize = 1000\n",
-    "    \n",
+    "\n",
     "    for idx,doc in enumerate(records,start=1):\n",
     "        # create byte vectors\n",
     "        docs.append(doc[\"product_text\"])\n",
@@ -548,7 +548,7 @@
     "            print(\"Vectors processed \", len(product_vectors), end='\\r')\n",
     "    product_vectors += get_embeddings(docs, EMBEDDING_MODEL)\n",
     "    print(\"Vectors processed \", len(product_vectors), end='\\r')\n",
-    "    return product_vectors"
+    "    return product_vectors\n"
    ]
   },
   {
@@ -562,7 +562,7 @@
     "    product_vectors = embeddings_batch_request(documents)\n",
     "    records = documents.to_dict(\"records\")\n",
     "    batchsize = 500\n",
-    "    \n",
+    "\n",
     "    # Use Redis pipelines to batch calls and save on round trip network communication\n",
     "    pipe = client.pipeline()\n",
     "    for idx,doc in enumerate(records,start=1):\n",
@@ -570,14 +570,14 @@
     "\n",
     "        # create byte vectors\n",
     "        text_embedding = np.array((product_vectors[idx-1]), dtype=np.float32).tobytes()\n",
-    "        \n",
+    "\n",
     "        # replace list of floats with byte vectors\n",
     "        doc[\"product_vector\"] = text_embedding\n",
     "\n",
     "        pipe.hset(key, mapping = doc)\n",
     "        if idx % batchsize == 0:\n",
     "            pipe.execute()\n",
-    "    pipe.execute()"
+    "    pipe.execute()\n"
    ]
   },
   {
@@ -600,7 +600,7 @@
    "source": [
     "%%time\n",
     "index_documents(redis_client, PREFIX, df)\n",
-    "print(f\"Loaded {redis_client.info()['db0']['keys']} documents in Redis search index with name: {INDEX_NAME}\")"
+    "print(f\"Loaded {redis_client.info()['db0']['keys']} documents in Redis search index with name: {INDEX_NAME}\")\n"
    ]
   },
   {
@@ -646,14 +646,14 @@
     "         .dialect(2)\n",
     "    )\n",
     "    params_dict = {\"vector\": np.array(embedded_query).astype(dtype=np.float32).tobytes()}\n",
-    "   \n",
+    "\n",
     "    # perform vector search\n",
     "    results = redis_client.ft(index_name).search(query, params_dict)\n",
     "    if print_results:\n",
     "        for i, product in enumerate(results.docs):\n",
     "            score = 1 - float(product.vector_score)\n",
     "            print(f\"{i}. {product.productDisplayName} (Score: {round(score ,3) })\")\n",
-    "    return results.docs"
+    "    return results.docs\n"
    ]
   },
   {
@@ -681,7 +681,7 @@
    ],
    "source": [
     "# Execute a simple vector search in Redis\n",
-    "results = search_redis(redis_client, 'man blue jeans', k=10)"
+    "results = search_redis(redis_client, 'man blue jeans', k=10)\n"
    ]
   },
   {
@@ -724,7 +724,7 @@
     "                       vector_field=\"product_vector\",\n",
     "                       k=10,\n",
     "                       hybrid_fields='@productDisplayName:\"blue jeans\"'\n",
-    "                       )"
+    "                       )\n"
    ]
   },
   {
@@ -755,7 +755,7 @@
     "                       vector_field=\"product_vector\",\n",
     "                       k=10,\n",
     "                       hybrid_fields='@productDisplayName:\"slim fit\"'\n",
-    "                       )"
+    "                       )\n"
    ]
   },
   {
@@ -788,7 +788,7 @@
     "                       vector_field=\"product_vector\",\n",
     "                       k=10,\n",
     "                       hybrid_fields='@masterCategory:{Accessories}'\n",
-    "                       )"
+    "                       )\n"
    ]
   },
   {
@@ -821,7 +821,7 @@
     "                       vector_field=\"product_vector\",\n",
     "                       k=10,\n",
     "                       hybrid_fields='@year:[2011 2012]'\n",
-    "                       )"
+    "                       )\n"
    ]
   },
   {
@@ -854,7 +854,7 @@
     "                       vector_field=\"product_vector\",\n",
     "                       k=10,\n",
     "                       hybrid_fields='(@year:[2011 2012] @season:{Summer})'\n",
-    "                       )"
+    "                       )\n"
    ]
   },
   {
@@ -883,7 +883,7 @@
     "                       vector_field=\"product_vector\",\n",
     "                       k=10,\n",
     "                       hybrid_fields='(@year:[2012 2012] @articleType:{Shirts | Belts} @productDisplayName:\"Wrangler\")'\n",
-    "                       )"
+    "                       )\n"
    ]
   }
  ],