diff --git a/tutorials/33_Hybrid_Retrieval.ipynb b/tutorials/33_Hybrid_Retrieval.ipynb
index d5e27294..8adf23f9 100644
--- a/tutorials/33_Hybrid_Retrieval.ipynb
+++ b/tutorials/33_Hybrid_Retrieval.ipynb
@@ -5,7 +5,15 @@
    "metadata": {
     "id": "kTas9ZQ7lXP7"
    },
-   "source": "# Tutorial: Creating a Hybrid Retrieval Pipeline\n\n- **Level**: Intermediate\n- **Time to complete**: 15 minutes\n- **Components Used**: [`DocumentSplitter`](https://docs.haystack.deepset.ai/docs/documentsplitter), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder), [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore), [`InMemoryBM25Retriever`](https://docs.haystack.deepset.ai/docs/inmemorybm25retriever), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever), and [`SentenceTransformersSimilarityRanker`](https://docs.haystack.deepset.ai/docs/sentencetransformerssimilarityranker)\n- **Prerequisites**: None\n- **Goal**: After completing this tutorial, you will have learned about creating a hybrid retrieval and when it's useful."
+   "source": [
+    "# Tutorial: Creating a Hybrid Retrieval Pipeline\n",
+    "\n",
+    "- **Level**: Intermediate\n",
+    "- **Time to complete**: 15 minutes\n",
+    "- **Components Used**: [`DocumentSplitter`](https://docs.haystack.deepset.ai/docs/documentsplitter), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder), [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore), [`InMemoryBM25Retriever`](https://docs.haystack.deepset.ai/docs/inmemorybm25retriever), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever), and [`SentenceTransformersSimilarityRanker`](https://docs.haystack.deepset.ai/docs/sentencetransformerssimilarityranker)\n",
+    "- **Prerequisites**: None\n",
+    "- **Goal**: After completing this tutorial, you will have learned about creating a hybrid retrieval and when it's useful."
+   ]
   },
   {
    "cell_type": "markdown",
@@ -38,7 +46,13 @@
     "id": "L40ZxZW8lXQh"
    },
    "outputs": [],
-   "source": "%%bash\n\npip install haystack-ai\npip install \"datasets>=2.6.1\"\npip install sentence-transformers-haystack"
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install haystack-ai\n",
+    "pip install \"datasets>=2.6.1\"\n",
+    "pip install sentence-transformers-haystack"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -144,7 +158,7 @@
     "\n",
     "document_splitter = DocumentSplitter(split_by=\"word\", split_length=512, split_overlap=32)\n",
     "document_embedder = SentenceTransformersDocumentEmbedder(\n",
-    "    model=\"BAAI/bge-small-en-v1.5\", device=ComponentDevice.from_str(\"cuda:0\")\n",
+    "    model=\"BAAI/bge-small-en-v1.5\", device=ComponentDevice.from_str(\"cuda:0\") # remove the device argument if you are not using a GPU\n",
     ")\n",
     "document_writer = DocumentWriter(document_store)\n",
     "\n",
@@ -194,7 +208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "id": "DVfQvnWYrMWr"
    },
@@ -204,7 +218,7 @@
     "from haystack_integrations.components.embedders.sentence_transformers import SentenceTransformersTextEmbedder\n",
     "\n",
     "text_embedder = SentenceTransformersTextEmbedder(\n",
-    "    model=\"BAAI/bge-small-en-v1.5\", device=ComponentDevice.from_str(\"cuda:0\")\n",
+    "    model=\"BAAI/bge-small-en-v1.5\", device=ComponentDevice.from_str(\"cuda:0\") # remove the device argument if you are not using a GPU\n",
     ")\n",
     "embedding_retriever = InMemoryEmbeddingRetriever(document_store)\n",
     "bm25_retriever = InMemoryBM25Retriever(document_store)"
@@ -215,7 +229,11 @@
    "metadata": {
     "id": "r8_jHzmosbC_"
    },
-   "source": "### 2) Rank the Results\n\nUse the [SentenceTransformersSimilarityRanker](https://docs.haystack.deepset.ai/docs/sentencetransformerssimilarityranker) that scores the relevancy of all retrieved documents for the given search query by using a cross encoder model. In this example, you will use [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) model to rank the retrieved documents but you can replace this model with other cross-encoder models on Hugging Face."
+   "source": [
+    "### 2) Rank the Results\n",
+    "\n",
+    "Use the [SentenceTransformersSimilarityRanker](https://docs.haystack.deepset.ai/docs/sentencetransformerssimilarityranker) that scores the relevancy of all retrieved documents for the given search query by using a cross encoder model. In this example, you will use [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) model to rank the retrieved documents but you can replace this model with other cross-encoder models on Hugging Face."
+   ]
   },
   {
    "cell_type": "code",
@@ -224,7 +242,11 @@
     "id": "cN0woIxHs4Ng"
    },
    "outputs": [],
-   "source": "from haystack_integrations.components.rankers.sentence_transformers import SentenceTransformersSimilarityRanker\n\nranker = SentenceTransformersSimilarityRanker(model=\"BAAI/bge-reranker-base\")"
+   "source": [
+    "from haystack_integrations.components.rankers.sentence_transformers import SentenceTransformersSimilarityRanker\n",
+    "\n",
+    "ranker = SentenceTransformersSimilarityRanker(model=\"BAAI/bge-reranker-base\")"
+   ]
   },
   {
    "cell_type": "markdown",
diff --git a/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb b/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
index 178b0cff..93df293c 100644
--- a/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
+++ b/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
@@ -51,7 +51,11 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": "%%bash\n\npip install haystack-ai wikipedia sentence-transformers-haystack"
+   "source": [
+    "%%bash\n",
+    "\n",
+    "pip install haystack-ai wikipedia sentence-transformers-haystack"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -83,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,8 +96,6 @@
     "from haystack_integrations.components.embedders.sentence_transformers import SentenceTransformersDocumentEmbedder\n",
     "from haystack.components.writers import DocumentWriter\n",
     "from haystack.document_stores.types import DuplicatePolicy\n",
-    "from haystack.utils import ComponentDevice\n",
-    "\n",
     "\n",
     "def create_indexing_pipeline(document_store, metadata_fields_to_embed=None):\n",
     "    document_cleaner = DocumentCleaner()\n",