diff --git a/ch3/jupyter-notebooks/components.ipynb b/ch3/jupyter-notebooks/components.ipynb
index d13e040..265effc 100644
--- a/ch3/jupyter-notebooks/components.ipynb
+++ b/ch3/jupyter-notebooks/components.ipynb
@@ -44,29 +44,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Calculating embeddings:   0%|          | 0/1 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Calculating embeddings: 100%|██████████| 1/1 [00:00<00:00,  3.18it/s]"
+      "Calculating embeddings: 100%|██████████| 1/1 [00:01<00:00,  1.02s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
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+      "[0.0016260817646980286, 0.005972211714833975]\n",
+      "[0.017629027366638184, -0.022774461656808853]\n"
      ]
     },
     {
@@ -99,1034 +92,65 @@
     "\n",
     "# Access the embeddings stored in the documents\n",
     "for doc in result['documents']:\n",
-    "    print(doc.embedding)"
+    "    print(doc.embedding[0:2])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Taking a look at the embeddings"
+    "Taking a look at the result data structure"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[0.0016260817646980286,\n",
-       " 0.005972211714833975,\n",
-       " -0.014899228699505329,\n",
-       " -0.008626528084278107,\n",
-       " -0.011499950662255287,\n",
-       " 0.015537766739726067,\n",
-       " -0.021434856578707695,\n",
-       " -0.008682869374752045,\n",
-       " -0.010905234143137932,\n",
-       " -0.022449005395174026,\n",
-       " 0.02130965329706669,\n",
-       " 0.017703790217638016,\n",
-       " 0.02211095578968525,\n",
-       " -0.0026777919847518206,\n",
-       " 0.0007449614349752665,\n",
-       " -0.012113448232412338,\n",
-       " 0.026142511516809464,\n",
-       " -0.008632788434624672,\n",
-       " 0.01363467238843441,\n",
-       " -0.012432717718183994,\n",
-       " -0.013446866534650326,\n",
-       " 0.023200226947665215,\n",
-       " -0.0051646484062075615,\n",
-       " -0.005230380687862635,\n",
-       " 0.007975469343364239,\n",
-       " 0.021046726033091545,\n",
-       " 0.010254175402224064,\n",
-       " 0.0007926953257992864,\n",
-       " 0.0027904752641916275,\n",
-       " -0.005781276151537895,\n",
-       " 0.015763133764266968,\n",
-       " -0.0021879328414797783,\n",
-       " -0.03615880012512207,\n",
-       " -0.0011683061020448804,\n",
-       " -0.010967835783958435,\n",
-       " -0.014122966676950455,\n",
-       " 0.0020392537117004395,\n",
-       " -0.006523107644170523,\n",
-       " 0.024226896464824677,\n",
-       " -0.04657573997974396,\n",
-       " 0.011506211012601852,\n",
-       " 0.021647702902555466,\n",
-       " -0.025604136288166046,\n",
-       " -0.005872048903256655,\n",
-       " 0.0022818357683718204,\n",
-       " 0.013872559182345867,\n",
-       " 0.010648566298186779,\n",
-       " -0.015562808141112328,\n",
-       " -0.03207716345787048,\n",
-       " 0.010341817513108253,\n",
-       " 0.001279424293898046,\n",
-       " 3.6803012335440144e-05,\n",
-       " -0.010116451419889927,\n",
-       " -0.008319779299199581,\n",
-       " -0.007512215990573168,\n",
-       " -0.011900602839887142,\n",
-       " -0.00790660735219717,\n",
-       " -0.0030909639317542315,\n",
-       " 0.014924269169569016,\n",
-       " -0.004779647570103407,\n",
-       " 0.005421315785497427,\n",
-       " 0.012620523571968079,\n",
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-       " -0.012032066471874714,\n",
-       " -0.03019910864531994,\n",
-       " -0.010354338213801384,\n",
-       " -0.003737327642738819,\n",
-       " -0.014786545187234879,\n",
-       " -0.006980100646615028,\n",
-       " 0.005696763750165701,\n",
-       " 0.018116962164640427,\n",
-       " -0.00928384717553854,\n",
-       " 0.0025619787629693747,\n",
-       " 0.028696665540337563,\n",
-       " 0.0126393036916852,\n",
-       " -0.010523363016545773,\n",
-       " 0.00014770112466067076,\n",
-       " -0.007925388403236866,\n",
-       " 0.020696155726909637,\n",
-       " -0.012589221820235252,\n",
-       " 0.004369605798274279,\n",
-       " 0.014836627058684826,\n",
-       " 0.02152249962091446,\n",
-       " 0.007130345329642296,\n",
-       " 0.00021323739201761782,\n",
-       " 0.019469160586595535,\n",
-       " 0.0011252673575654626,\n",
-       " -0.005089526530355215,\n",
-       " -0.013860038481652737,\n",
-       " 0.012689385563135147,\n",
-       " 0.02350071631371975,\n",
-       " ...]"
+       "{'documents': [Document(id='2e3218009b01cfc57f865bbf81fa70de81b5ebae02c4cc7092e46ffde03f3c49', content='The quick brown fox jumps over the lazy dog.', dataframe=None, blob=None, meta={}, score=None),\n",
+       "  Document(id='63a06e3e867cb70e52a99c00b2de17fe531431c98e7d851268be01d341ea9f20', content='To be or not to be, that is the question.', dataframe=None, blob=None, meta={}, score=None)],\n",
+       " 'metadata': {'model': 'text-embedding-ada-002-v2',\n",
+       "  'usage': {'prompt_tokens': 22, 'total_tokens': 22}}}"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "result['documents'][0].embedding"
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The metadata shows the model and usage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'model': 'text-embedding-ada-002-v2',\n",
+       " 'usage': {'prompt_tokens': 22, 'total_tokens': 22}}"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result['metadata']"
    ]
   },
   {
@@ -1138,6 +162,11 @@
     "In this snippet, `text_embedder` is created with an OpenAI API key and used to generate an embedding for the string \"I love pizza!\". The resulting embedding and associated metadata are then printed out."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 5,