visualization file formatted

bootcamp/tutorials/quickstart/rag_visualization.ipynb

@@ -151,7 +151,7 @@
     "    with open(file_path, \"r\") as file:\n",
     "        file_text = file.read()\n",
     "\n",
-    "    text_lines += file_text.split(\"# \")\n"
+    "    text_lines += file_text.split(\"# \")"
    ]
   },
   {
@@ -171,7 +171,7 @@
    "source": [
     "from openai import OpenAI\n",
     "\n",
-    "openai_client = OpenAI()\n"
+    "openai_client = OpenAI()"
    ]
   },
   {
@@ -265,7 +265,7 @@
    "outputs": [],
    "source": [
     "if milvus_client.has_collection(collection_name):\n",
-    "    milvus_client.drop_collection(collection_name)\n"
+    "    milvus_client.drop_collection(collection_name)"
    ]
   },
   {
@@ -288,7 +288,7 @@
     "    dimension=embedding_dim,\n",
     "    metric_type=\"IP\",  # Inner product distance\n",
     "    consistency_level=\"Strong\",  # Strong consistency level\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -333,7 +333,7 @@
     "for i, line in enumerate(tqdm(text_lines, desc=\"Creating embeddings\")):\n",
     "    data.append({\"id\": i, \"vector\": emb_text(line), \"text\": line})\n",
     "\n",
-    "milvus_client.insert(collection_name=collection_name, data=data)\n"
+    "milvus_client.insert(collection_name=collection_name, data=data)"
    ]
   },
   {
@@ -382,7 +382,7 @@
     "    limit=10,  # Return top 3 results\n",
     "    search_params={\"metric_type\": \"IP\", \"params\": {}},  # Inner product distance\n",
     "    output_fields=[\"text\"],  # Return the text field\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -452,7 +452,7 @@
     "retrieved_lines_with_distances = [\n",
     "    (res[\"entity\"][\"text\"], res[\"distance\"]) for res in search_res[0]\n",
     "]\n",
-    "print(json.dumps(retrieved_lines_with_distances, indent=4))\n"
+    "print(json.dumps(retrieved_lines_with_distances, indent=4))"
    ]
   },
   {
@@ -582,16 +582,16 @@
     "import numpy as np\n",
     "from sklearn.manifold import TSNE\n",
     "\n",
-    "data.append({'id': len(data), 'vector': emb_text(question), 'text': question})\n",
+    "data.append({\"id\": len(data), \"vector\": emb_text(question), \"text\": question})\n",
     "embeddings = []\n",
     "for gp in data:\n",
-    "    embeddings.append(gp['vector'])\n",
+    "    embeddings.append(gp[\"vector\"])\n",
     "\n",
     "X = np.array(embeddings, dtype=np.float32)\n",
     "tsne = TSNE(random_state=0, max_iter=1000)\n",
     "tsne_results = tsne.fit_transform(X)\n",
     "\n",
-    "df_tsne = pd.DataFrame(tsne_results, columns=['TSNE1', 'TSNE2'])\n",
+    "df_tsne = pd.DataFrame(tsne_results, columns=[\"TSNE1\", \"TSNE2\"])\n",
     "df_tsne"
    ]
   },
@@ -623,7 +623,7 @@
     "import seaborn as sns\n",
     "\n",
     "# Extract similar ids from search results\n",
-    "similar_ids = [gp['id'] for gp in search_res[0]]\n",
+    "similar_ids = [gp[\"id\"] for gp in search_res[0]]\n",
     "\n",
     "df_norm = df_tsne[:-1]\n",
     "\n",
@@ -636,23 +636,34 @@
     "fig, ax = plt.subplots(figsize=(8, 6))  # Set figsize\n",
     "\n",
     "# Set the style of the plot\n",
-    "sns.set_style('darkgrid', {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
+    "sns.set_style(\"darkgrid\", {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
     "\n",
     "# Plot all points in blue\n",
-    "sns.scatterplot(data=df_tsne, x='TSNE1', y='TSNE2', color='blue', label='All knowledge', ax=ax)\n",
+    "sns.scatterplot(\n",
+    "    data=df_tsne, x=\"TSNE1\", y=\"TSNE2\", color=\"blue\", label=\"All knowledge\", ax=ax\n",
+    ")\n",
     "\n",
     "# Overlay similar points in red\n",
-    "sns.scatterplot(data=similar_points, x='TSNE1', y='TSNE2', color='red', label='Similar knowledge', ax=ax)\n",
-    "\n",
-    "sns.scatterplot(data=df_query, x='TSNE1', y='TSNE2', color='green', label='Query', ax=ax)\n",
+    "sns.scatterplot(\n",
+    "    data=similar_points,\n",
+    "    x=\"TSNE1\",\n",
+    "    y=\"TSNE2\",\n",
+    "    color=\"red\",\n",
+    "    label=\"Similar knowledge\",\n",
+    "    ax=ax,\n",
+    ")\n",
+    "\n",
+    "sns.scatterplot(\n",
+    "    data=df_query, x=\"TSNE1\", y=\"TSNE2\", color=\"green\", label=\"Query\", ax=ax\n",
+    ")\n",
     "\n",
     "# Set plot titles and labels\n",
-    "plt.title('Scatter plot of knowledge using t-SNE')\n",
-    "plt.xlabel('TSNE1')\n",
-    "plt.ylabel('TSNE2')\n",
+    "plt.title(\"Scatter plot of knowledge using t-SNE\")\n",
+    "plt.xlabel(\"TSNE1\")\n",
+    "plt.ylabel(\"TSNE2\")\n",
     "\n",
     "# Set axis to be equal\n",
-    "plt.axis('equal')\n",
+    "plt.axis(\"equal\")\n",
     "\n",
     "# Display the legend\n",
     "plt.legend()\n",