diff --git a/ch3/jupyter-notebooks/components.ipynb b/ch3/jupyter-notebooks/components.ipynb
index 265effc..07e8215 100644
--- a/ch3/jupyter-notebooks/components.ipynb
+++ b/ch3/jupyter-notebooks/components.ipynb
@@ -44,21 +44,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Calculating embeddings: 100%|██████████| 1/1 [00:01<00:00,  1.02s/it]"
+      "Calculating embeddings:   0%|          | 0/1 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculating embeddings: 100%|██████████| 1/1 [00:00<00:00,  1.86it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.0016260817646980286, 0.005972211714833975]\n",
+      "[0.0015798426466062665, 0.0059846313670277596]\n",
       "[0.017629027366638184, -0.022774461656808853]\n"
      ]
     },
@@ -86,7 +93,7 @@
     "\n",
     "# Initialize the embedder with your OpenAI API key\n",
     "document_embedder = OpenAIDocumentEmbedder(api_key=api_key)\n",
-    "\n",
+    "\"\"\n",
     "# Run the embedder to get embeddings\n",
     "result = document_embedder.run(docs)\n",
     "\n",
@@ -104,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -116,7 +123,7 @@
        "  'usage': {'prompt_tokens': 22, 'total_tokens': 22}}}"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -134,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -144,7 +151,7 @@
        " 'usage': {'prompt_tokens': 22, 'total_tokens': 22}}"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -169,17 +176,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from haystack.preview.components.embedders import OpenAITextEmbedder\n",
     "\n",
@@ -190,8 +189,7 @@
     "text_to_embed = \"I love pizza!\"\n",
     "\n",
     "# Embed the text and print the result\n",
-    "result = text_embedder.run(text_to_embed)\n",
-    "print(result)\n",
+    "result_text= text_embedder.run(text_to_embed)\n",
     "\n",
     "# Output will include the embedding vector and metadata\n",
     "# {'embedding': [0.017020374536514282, -0.023255806416273117, ...],\n",
@@ -199,6 +197,74 @@
     "#               'usage': {'prompt_tokens': 4, 'total_tokens': 4}}}\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['embedding', 'metadata'])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_text.keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As before, we can access the embeddings through the embedding key"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.017020374536514282, -0.023255806416273117]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_text['embedding'][0:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'model': 'text-embedding-ada-002-v2',\n",
+       " 'usage': {'prompt_tokens': 4, 'total_tokens': 4}}"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_text['metadata']"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,