diff --git a/src/llm_utilikit/LangChain/notebooks/langchain-embeddings-retrieval-agent.ipynb b/src/llm_utilikit/LangChain/notebooks/langchain-embeddings-retrieval-agent.ipynb
index 97b4e31..75d486b 100644
--- a/src/llm_utilikit/LangChain/notebooks/langchain-embeddings-retrieval-agent.ipynb
+++ b/src/llm_utilikit/LangChain/notebooks/langchain-embeddings-retrieval-agent.ipynb
@@ -3,8 +3,8 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "view-in-github"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/gist/Daethyra/0e3f515a41d78d89babbea00c057b8d2/langchain-embeddings-retrieval-agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -44,84 +44,38 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 7,
       "metadata": {
-        "id": "pva9ehKXUpU2",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
+        "id": "pva9ehKXUpU2",
         "outputId": "21af5614-b078-415d-8aa3-9efd125b4757"
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
           "text": [
-            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/72.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m71.7/72.0 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.0/72.0 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m25.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m223.6/223.6 kB\u001b[0m \u001b[31m25.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m223.0/223.0 kB\u001b[0m \u001b[31m23.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m218.0/218.0 kB\u001b[0m \u001b[31m24.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.7/211.7 kB\u001b[0m \u001b[31m23.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m341.8/341.8 kB\u001b[0m \u001b[31m30.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.4/143.4 kB\u001b[0m \u001b[31m17.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m121.4/121.4 kB\u001b[0m \u001b[31m15.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m120.3/120.3 kB\u001b[0m \u001b[31m15.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.6/115.6 kB\u001b[0m \u001b[31m14.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.5/115.5 kB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.1/115.1 kB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.6/114.6 kB\u001b[0m \u001b[31m13.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
-            "google-cloud-bigquery 3.10.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.3 which is incompatible.\n",
-            "google-cloud-bigquery-connection 1.12.1 requires google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.0, but you have google-api-core 2.8.2 which is incompatible.\n",
-            "google-cloud-bigquery-connection 1.12.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.3 which is incompatible.\n",
-            "google-cloud-bigquery-storage 2.22.0 requires google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.0, but you have google-api-core 2.8.2 which is incompatible.\n",
-            "google-cloud-bigquery-storage 2.22.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.3 which is incompatible.\n",
-            "google-cloud-datastore 2.15.2 requires google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.0, but you have google-api-core 2.8.2 which is incompatible.\n",
-            "google-cloud-datastore 2.15.2 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.3 which is incompatible.\n",
-            "google-cloud-firestore 2.11.1 requires google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.0, but you have google-api-core 2.8.2 which is incompatible.\n",
-            "google-cloud-firestore 2.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.3 which is incompatible.\n",
-            "google-cloud-functions 1.13.3 requires google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.0, but you have google-api-core 2.8.2 which is incompatible.\n",
-            "google-cloud-functions 1.13.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.3 which is incompatible.\n",
-            "google-cloud-language 2.9.1 requires google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.0, but you have google-api-core 2.8.2 which is incompatible.\n",
-            "google-cloud-language 2.9.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.3 which is incompatible.\n",
-            "google-cloud-translate 3.11.3 requires google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.0, but you have google-api-core 2.8.2 which is incompatible.\n",
-            "google-cloud-translate 3.11.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.3 which is incompatible.\n",
-            "google-colab 1.0.0 requires pandas==1.5.3, but you have pandas 2.1.1 which is incompatible.\n",
-            "grpc-google-iam-v1 0.12.6 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.19.3 which is incompatible.\n",
-            "pandas-gbq 0.17.9 requires pyarrow<10.0dev,>=3.0.0, but you have pyarrow 11.0.0 which is incompatible.\n",
-            "tensorboard 2.13.0 requires protobuf>=3.19.6, but you have protobuf 3.19.3 which is incompatible.\n",
-            "tensorflow 2.13.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.19.3 which is incompatible.\n",
-            "tensorflow-datasets 4.9.3 requires protobuf>=3.20, but you have protobuf 3.19.3 which is incompatible.\n",
-            "tensorflow-hub 0.14.0 requires protobuf>=3.19.6, but you have protobuf 3.19.3 which is incompatible.\n",
-            "tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 3.19.3 which is incompatible.\u001b[0m\u001b[31m\n",
-            "\u001b[0m"
+            "  WARNING: Failed to remove contents in a temporary directory 'C:\\Users\\dae\\.vscode\\Software\\.venv\\Lib\\site-packages\\google\\~rotobuf'.\n",
+            "  You can safely remove it manually.\n"
           ]
         }
       ],
       "source": [
-        "!pip install -qU \\\n",
-        "    openai==0.27.7 \\\n",
-        "    \"pinecone-client[grpc]\"==2.2.1 \\\n",
-        "    pinecone-datasets==0.5.1 \\\n",
-        "    langchain==0.0.162 \\\n",
-        "    tiktoken==0.4.0"
+        "%pip install -qU \\\n",
+        "    openai \\\n",
+        "    \"pinecone-client[grpc]\" \\\n",
+        "    pinecone-datasets \\\n",
+        "    langchain \\\n",
+        "    tiktoken"
       ]
     },
     {
@@ -144,7 +98,54 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 8,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['ANN_DEEP1B_d96_angular',\n",
+              " 'ANN_Fashion-MNIST_d784_euclidean',\n",
+              " 'ANN_GIST_d960_euclidean',\n",
+              " 'ANN_GloVe_d100_angular',\n",
+              " 'ANN_GloVe_d200_angular',\n",
+              " 'ANN_GloVe_d25_angular',\n",
+              " 'ANN_GloVe_d50_angular',\n",
+              " 'ANN_LastFM_d64_angular',\n",
+              " 'ANN_MNIST_d784_euclidean',\n",
+              " 'ANN_NYTimes_d256_angular',\n",
+              " 'ANN_SIFT1M_d128_euclidean',\n",
+              " 'amazon_toys_quora_all-MiniLM-L6-bm25',\n",
+              " 'it-threat-data-test',\n",
+              " 'it-threat-data-train',\n",
+              " 'langchain-python-docs-text-embedding-ada-002',\n",
+              " 'movielens-user-ratings',\n",
+              " 'msmarco-v1-bm25-allMiniLML6V2',\n",
+              " 'quora_all-MiniLM-L6-bm25-100K',\n",
+              " 'quora_all-MiniLM-L6-bm25',\n",
+              " 'quora_all-MiniLM-L6-v2_Splade-100K',\n",
+              " 'quora_all-MiniLM-L6-v2_Splade',\n",
+              " 'squad-text-embedding-ada-002',\n",
+              " 'wikipedia-simple-text-embedding-ada-002-100K',\n",
+              " 'wikipedia-simple-text-embedding-ada-002',\n",
+              " 'youtube-transcripts-text-embedding-ada-002']"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from pinecone_datasets import load_dataset, list_datasets\n",
+        "\n",
+        "# Check available datasets\n",
+        "list_datasets()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -155,33 +156,103 @@
       },
       "outputs": [
         {
-          "output_type": "execute_result",
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "_request non-retriable exception: Invalid bucket name: 'pinecone-datasets-dev\\squad-text-embedding-ada-002', 400\n",
+            "Traceback (most recent call last):\n",
+            "  File \"c:\\Users\\dae\\.vscode\\Software\\.venv\\lib\\site-packages\\gcsfs\\retry.py\", line 123, in retry_request\n",
+            "    return await func(*args, **kwargs)\n",
+            "  File \"c:\\Users\\dae\\.vscode\\Software\\.venv\\lib\\site-packages\\gcsfs\\core.py\", line 430, in _request\n",
+            "    validate_response(status, contents, path, args)\n",
+            "  File \"c:\\Users\\dae\\.vscode\\Software\\.venv\\lib\\site-packages\\gcsfs\\retry.py\", line 110, in validate_response\n",
+            "    raise HttpError(error)\n",
+            "gcsfs.retry.HttpError: Invalid bucket name: 'pinecone-datasets-dev\\squad-text-embedding-ada-002', 400\n"
+          ]
+        },
+        {
+          "ename": "HttpError",
+          "evalue": "Invalid bucket name: 'pinecone-datasets-dev\\squad-text-embedding-ada-002', 400",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[1;31mHttpError\u001b[0m                                 Traceback (most recent call last)",
+            "Cell \u001b[1;32mIn[10], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m dataset \u001b[38;5;241m=\u001b[39m \u001b[43mload_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msquad-text-embedding-ada-002\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m dataset\u001b[38;5;241m.\u001b[39mhead()\n",
+            "File \u001b[1;32mc:\\Users\\dae\\.vscode\\Software\\.venv\\lib\\site-packages\\pinecone_datasets\\public.py:59\u001b[0m, in \u001b[0;36mload_dataset\u001b[1;34m(dataset_id, **kwargs)\u001b[0m\n\u001b[0;32m     57\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdataset_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found in catalog\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     58\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m---> 59\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m Dataset\u001b[38;5;241m.\u001b[39mfrom_catalog(dataset_id, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+            "File \u001b[1;32mc:\\Users\\dae\\.vscode\\Software\\.venv\\lib\\site-packages\\pinecone_datasets\\dataset.py:112\u001b[0m, in \u001b[0;36mDataset.from_catalog\u001b[1;34m(cls, dataset_id, catalog_base_path, **kwargs)\u001b[0m\n\u001b[0;32m    106\u001b[0m catalog_base_path \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    107\u001b[0m     catalog_base_path\n\u001b[0;32m    108\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m catalog_base_path\n\u001b[0;32m    109\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m os\u001b[38;5;241m.\u001b[39menviron\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDATASETS_CATALOG_BASEPATH\u001b[39m\u001b[38;5;124m\"\u001b[39m, cfg\u001b[38;5;241m.\u001b[39mStorage\u001b[38;5;241m.\u001b[39mendpoint)\n\u001b[0;32m    110\u001b[0m )\n\u001b[0;32m    111\u001b[0m dataset_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(catalog_base_path, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdataset_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 112\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m(dataset_path\u001b[38;5;241m=\u001b[39mdataset_path, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+            "File \u001b[1;32mc:\\Users\\dae\\.vscode\\Software\\.venv\\lib\\site-packages\\pinecone_datasets\\dataset.py:212\u001b[0m, in \u001b[0;36mDataset.__init__\u001b[1;34m(self, dataset_path, **kwargs)\u001b[0m\n\u001b[0;32m    210\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fs \u001b[38;5;241m=\u001b[39m get_cloud_fs(endpoint, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    211\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_path \u001b[38;5;241m=\u001b[39m dataset_path\n\u001b[1;32m--> 212\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexists\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_path\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m    213\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\n\u001b[0;32m    214\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset does not exist. Please check the path or dataset_id\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    215\u001b[0m         )\n\u001b[0;32m    216\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+            "File \u001b[1;32mc:\\Users\\dae\\.vscode\\Software\\.venv\\lib\\site-packages\\fsspec\\asyn.py:118\u001b[0m, in \u001b[0;36msync_wrapper.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    115\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m    116\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m    117\u001b[0m     \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m obj \u001b[38;5;129;01mor\u001b[39;00m args[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m--> 118\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m sync(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloop, func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+            "File \u001b[1;32mc:\\Users\\dae\\.vscode\\Software\\.venv\\lib\\site-packages\\fsspec\\asyn.py:103\u001b[0m, in \u001b[0;36msync\u001b[1;34m(loop, func, timeout, *args, **kwargs)\u001b[0m\n\u001b[0;32m    101\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m FSTimeoutError \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mreturn_result\u001b[39;00m\n\u001b[0;32m    102\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(return_result, \u001b[38;5;167;01mBaseException\u001b[39;00m):\n\u001b[1;32m--> 103\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m return_result\n\u001b[0;32m    104\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    105\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m return_result\n",
+            "File \u001b[1;32mc:\\Users\\dae\\.vscode\\Software\\.venv\\lib\\site-packages\\fsspec\\asyn.py:56\u001b[0m, in \u001b[0;36m_runner\u001b[1;34m(event, coro, result, timeout)\u001b[0m\n\u001b[0;32m     54\u001b[0m     coro \u001b[38;5;241m=\u001b[39m asyncio\u001b[38;5;241m.\u001b[39mwait_for(coro, timeout\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 56\u001b[0m     result[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m coro\n\u001b[0;32m     57\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m ex:\n\u001b[0;32m     58\u001b[0m     result[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m ex\n",
+            "File \u001b[1;32mc:\\Users\\dae\\.vscode\\Software\\.venv\\lib\\site-packages\\fsspec\\asyn.py:677\u001b[0m, in \u001b[0;36mAsyncFileSystem._exists\u001b[1;34m(self, path, **kwargs)\u001b[0m\n\u001b[0;32m    675\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_exists\u001b[39m(\u001b[38;5;28mself\u001b[39m, path, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m    676\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 677\u001b[0m         \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info(path, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    678\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m    679\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m:\n",
+            "File \u001b[1;32mc:\\Users\\dae\\.vscode\\Software\\.venv\\lib\\site-packages\\gcsfs\\core.py:926\u001b[0m, in \u001b[0;36mGCSFileSystem._info\u001b[1;34m(self, path, generation, **kwargs)\u001b[0m\n\u001b[0;32m    924\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m path:\n\u001b[0;32m    925\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 926\u001b[0m         out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGET\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, json_out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m    927\u001b[0m         out\u001b[38;5;241m.\u001b[39mupdate(size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdirectory\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    928\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n\u001b[0;32m    929\u001b[0m         \u001b[38;5;66;03m# GET bucket failed, try ls; will have no metadata\u001b[39;00m\n",
+            "File \u001b[1;32mc:\\Users\\dae\\.vscode\\Software\\.venv\\lib\\site-packages\\gcsfs\\core.py:437\u001b[0m, in \u001b[0;36mGCSFileSystem._call\u001b[1;34m(self, method, path, json_out, info_out, *args, **kwargs)\u001b[0m\n\u001b[0;32m    433\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_call\u001b[39m(\n\u001b[0;32m    434\u001b[0m     \u001b[38;5;28mself\u001b[39m, method, path, \u001b[38;5;241m*\u001b[39margs, json_out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, info_out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m    435\u001b[0m ):\n\u001b[0;32m    436\u001b[0m     logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmethod\u001b[38;5;241m.\u001b[39mupper()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00margs\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mheaders\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 437\u001b[0m     status, headers, info, contents \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_request(\n\u001b[0;32m    438\u001b[0m         method, path, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m    439\u001b[0m     )\n\u001b[0;32m    440\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m json_out:\n\u001b[0;32m    441\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m json\u001b[38;5;241m.\u001b[39mloads(contents)\n",
+            "File \u001b[1;32mc:\\Users\\dae\\.vscode\\Software\\.venv\\lib\\site-packages\\decorator.py:221\u001b[0m, in \u001b[0;36mdecorate.<locals>.fun\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m    219\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kwsyntax:\n\u001b[0;32m    220\u001b[0m     args, kw \u001b[38;5;241m=\u001b[39m fix(args, kw, sig)\n\u001b[1;32m--> 221\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m caller(func, \u001b[38;5;241m*\u001b[39m(extras \u001b[38;5;241m+\u001b[39m args), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw)\n",
+            "File \u001b[1;32mc:\\Users\\dae\\.vscode\\Software\\.venv\\lib\\site-packages\\gcsfs\\retry.py:158\u001b[0m, in \u001b[0;36mretry_request\u001b[1;34m(func, retries, *args, **kwargs)\u001b[0m\n\u001b[0;32m    156\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m    157\u001b[0m logger\u001b[38;5;241m.\u001b[39mexception(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m non-retriable exception: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 158\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n",
+            "File \u001b[1;32mc:\\Users\\dae\\.vscode\\Software\\.venv\\lib\\site-packages\\gcsfs\\retry.py:123\u001b[0m, in \u001b[0;36mretry_request\u001b[1;34m(func, retries, *args, **kwargs)\u001b[0m\n\u001b[0;32m    121\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m retry \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m    122\u001b[0m         \u001b[38;5;28;01mawait\u001b[39;00m asyncio\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;28mmin\u001b[39m(random\u001b[38;5;241m.\u001b[39mrandom() \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m (retry \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m), \u001b[38;5;241m32\u001b[39m))\n\u001b[1;32m--> 123\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    124\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\n\u001b[0;32m    125\u001b[0m     HttpError,\n\u001b[0;32m    126\u001b[0m     requests\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mRequestException,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    129\u001b[0m     aiohttp\u001b[38;5;241m.\u001b[39mclient_exceptions\u001b[38;5;241m.\u001b[39mClientError,\n\u001b[0;32m    130\u001b[0m ) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    131\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m    132\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(e, HttpError)\n\u001b[0;32m    133\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m e\u001b[38;5;241m.\u001b[39mcode \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m400\u001b[39m\n\u001b[0;32m    134\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequester pays\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m e\u001b[38;5;241m.\u001b[39mmessage\n\u001b[0;32m    135\u001b[0m     ):\n",
+            "File \u001b[1;32mc:\\Users\\dae\\.vscode\\Software\\.venv\\lib\\site-packages\\gcsfs\\core.py:430\u001b[0m, in \u001b[0;36mGCSFileSystem._request\u001b[1;34m(self, method, path, headers, json, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m    427\u001b[0m info \u001b[38;5;241m=\u001b[39m r\u001b[38;5;241m.\u001b[39mrequest_info  \u001b[38;5;66;03m# for debug only\u001b[39;00m\n\u001b[0;32m    428\u001b[0m contents \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m r\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m--> 430\u001b[0m \u001b[43mvalidate_response\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstatus\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontents\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    431\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m status, headers, info, contents\n",
+            "File \u001b[1;32mc:\\Users\\dae\\.vscode\\Software\\.venv\\lib\\site-packages\\gcsfs\\retry.py:110\u001b[0m, in \u001b[0;36mvalidate_response\u001b[1;34m(status, content, path, args)\u001b[0m\n\u001b[0;32m    108\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBad Request: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mmsg\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    109\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m error:\n\u001b[1;32m--> 110\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m HttpError(error)\n\u001b[0;32m    111\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m status:\n\u001b[0;32m    112\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m HttpError({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcode\u001b[39m\u001b[38;5;124m\"\u001b[39m: status, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessage\u001b[39m\u001b[38;5;124m\"\u001b[39m: msg})  \u001b[38;5;66;03m# text-like\u001b[39;00m\n",
+            "\u001b[1;31mHttpError\u001b[0m: Invalid bucket name: 'pinecone-datasets-dev\\squad-text-embedding-ada-002', 400"
+          ]
+        }
+      ],
+      "source": [
+        "dataset = load_dataset(\"squad-text-embedding-ada-002\")\n",
+        "dataset.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "K5Q16wRH9SmO",
+        "outputId": "fdf2947f-a270-4417-a02b-057e62356dfe"
+      },
+      "outputs": [
+        {
           "data": {
             "text/plain": [
-              "                         id  \\\n",
-              "0  5733be284776f41900661182   \n",
-              "1  5733bf84d058e614000b61be   \n",
-              "2  5733bed24776f41900661188   \n",
-              "3  5733a6424776f41900660f51   \n",
-              "4  5733a70c4776f41900660f64   \n",
-              "\n",
-              "                                              values sparse_values  \\\n",
-              "0  [-0.010262451963272523, 0.02222637996192584, -...          None   \n",
-              "1  [-0.009786712423983223, -0.013988726438873078,...          None   \n",
-              "2  [0.013343917696606181, -0.0007001232846109822,...          None   \n",
-              "3  [-0.0085222901071539, 0.004399558219521822, -0...          None   \n",
-              "4  [-0.006695996885869355, -0.02067068565761649, ...          None   \n",
-              "\n",
-              "                                            metadata  blob  \n",
-              "0  {'text': 'Architecturally, the school has a Ca...  None  \n",
-              "1  {'text': 'As at most other universities, Notre...  None  \n",
-              "2  {'text': 'The university is the major seat of ...  None  \n",
-              "3  {'text': 'The College of Engineering was estab...  None  \n",
-              "4  {'text': 'All of Notre Dame's undergraduate st...  None  "
-            ],
+              "18891"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "len(dataset)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c3-Plec39SmO"
+      },
+      "source": [
+        "We'll format the dataset ready for upsert and reduce what we use to a subset of the full dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "4CW5mNi89SmO",
+        "outputId": "b7485e0d-1aa6-4f2b-840e-fdef19d58ffc"
+      },
+      "outputs": [
+        {
+          "data": {
             "text/html": [
               "\n",
-              "  <div id=\"df-0fdba9f0-1dc5-4930-aa6a-45073c217038\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-ae493f02-d341-44ae-b022-ef7c62f7ae4d\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -202,9 +273,7 @@
               "      <th></th>\n",
               "      <th>id</th>\n",
               "      <th>values</th>\n",
-              "      <th>sparse_values</th>\n",
               "      <th>metadata</th>\n",
-              "      <th>blob</th>\n",
               "    </tr>\n",
               "  </thead>\n",
               "  <tbody>\n",
@@ -212,41 +281,31 @@
               "      <th>0</th>\n",
               "      <td>5733be284776f41900661182</td>\n",
               "      <td>[-0.010262451963272523, 0.02222637996192584, -...</td>\n",
-              "      <td>None</td>\n",
               "      <td>{'text': 'Architecturally, the school has a Ca...</td>\n",
-              "      <td>None</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1</th>\n",
               "      <td>5733bf84d058e614000b61be</td>\n",
               "      <td>[-0.009786712423983223, -0.013988726438873078,...</td>\n",
-              "      <td>None</td>\n",
               "      <td>{'text': 'As at most other universities, Notre...</td>\n",
-              "      <td>None</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>2</th>\n",
               "      <td>5733bed24776f41900661188</td>\n",
               "      <td>[0.013343917696606181, -0.0007001232846109822,...</td>\n",
-              "      <td>None</td>\n",
               "      <td>{'text': 'The university is the major seat of ...</td>\n",
-              "      <td>None</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>3</th>\n",
               "      <td>5733a6424776f41900660f51</td>\n",
               "      <td>[-0.0085222901071539, 0.004399558219521822, -0...</td>\n",
-              "      <td>None</td>\n",
               "      <td>{'text': 'The College of Engineering was estab...</td>\n",
-              "      <td>None</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>4</th>\n",
               "      <td>5733a70c4776f41900660f64</td>\n",
               "      <td>[-0.006695996885869355, -0.02067068565761649, ...</td>\n",
-              "      <td>None</td>\n",
               "      <td>{'text': 'All of Notre Dame's undergraduate st...</td>\n",
-              "      <td>None</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
@@ -254,7 +313,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0fdba9f0-1dc5-4930-aa6a-45073c217038')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae493f02-d341-44ae-b022-ef7c62f7ae4d')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -306,12 +365,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-0fdba9f0-1dc5-4930-aa6a-45073c217038 button.colab-df-convert');\n",
+              "        document.querySelector('#df-ae493f02-d341-44ae-b022-ef7c62f7ae4d button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-0fdba9f0-1dc5-4930-aa6a-45073c217038');\n",
+              "        const element = document.querySelector('#df-ae493f02-d341-44ae-b022-ef7c62f7ae4d');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -331,8 +390,8 @@
               "  </div>\n",
               "\n",
               "\n",
-              "<div id=\"df-302387b5-69bc-4657-8569-e2dde97c1a10\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-302387b5-69bc-4657-8569-e2dde97c1a10')\"\n",
+              "<div id=\"df-2794b672-1cdf-41a1-8f1b-4d58ca5d2ecb\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2794b672-1cdf-41a1-8f1b-4d58ca5d2ecb')\"\n",
               "            title=\"Suggest charts.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -451,7 +510,7 @@
               "    }\n",
               "    (() => {\n",
               "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-302387b5-69bc-4657-8569-e2dde97c1a10 button');\n",
+              "        document.querySelector('#df-2794b672-1cdf-41a1-8f1b-4d58ca5d2ecb button');\n",
               "      quickchartButtonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "    })();\n",
@@ -459,19 +518,124 @@
               "</div>\n",
               "    </div>\n",
               "  </div>\n"
+            ],
+            "text/plain": [
+              "                         id  \\\n",
+              "0  5733be284776f41900661182   \n",
+              "1  5733bf84d058e614000b61be   \n",
+              "2  5733bed24776f41900661188   \n",
+              "3  5733a6424776f41900660f51   \n",
+              "4  5733a70c4776f41900660f64   \n",
+              "\n",
+              "                                              values  \\\n",
+              "0  [-0.010262451963272523, 0.02222637996192584, -...   \n",
+              "1  [-0.009786712423983223, -0.013988726438873078,...   \n",
+              "2  [0.013343917696606181, -0.0007001232846109822,...   \n",
+              "3  [-0.0085222901071539, 0.004399558219521822, -0...   \n",
+              "4  [-0.006695996885869355, -0.02067068565761649, ...   \n",
+              "\n",
+              "                                            metadata  \n",
+              "0  {'text': 'Architecturally, the school has a Ca...  \n",
+              "1  {'text': 'As at most other universities, Notre...  \n",
+              "2  {'text': 'The university is the major seat of ...  \n",
+              "3  {'text': 'The College of Engineering was estab...  \n",
+              "4  {'text': 'All of Notre Dame's undergraduate st...  "
             ]
           },
+          "execution_count": 4,
           "metadata": {},
-          "execution_count": 2
+          "output_type": "execute_result"
         }
       ],
       "source": [
-        "from pinecone_datasets import load_dataset\n",
+        "# we drop sparse_values as they are not needed for this example\n",
+        "dataset.documents.drop(['sparse_values', 'blob'], axis=1, inplace=True)\n",
         "\n",
-        "dataset = load_dataset(\"squad-text-embedding-ada-002\")\n",
         "dataset.head()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "B2_Pt7N6Zg2X"
+      },
+      "source": [
+        "## Vector Database"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JQTfOTR6aBRS"
+      },
+      "source": [
+        "Next we initialize a Pinecone vector database. For this we need a [free API key](https://app.pinecone.io/), then we create the index:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "C3wrG-9yaJel"
+      },
+      "outputs": [],
+      "source": [
+        "import pinecone\n",
+        "import os\n",
+        "\n",
+        "# Load Pinecone API key\n",
+        "api_key = os.getenv('PINECONE_API_KEY') or 'api_key'\n",
+        "# Set Pinecone environment. Find next to API key in console\n",
+        "env = os.getenv('PINECONE_ENVIRONMENT') or \"us-central1-gcp\"\n",
+        "\n",
+        "pinecone.init(api_key=api_key, environment=env)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lgfywcQj9SmP"
+      },
+      "outputs": [],
+      "source": [
+        "index_name = 'langchain-retrieval-agent-fast'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "D5WT4PAN9SmP"
+      },
+      "outputs": [],
+      "source": [
+        "import time\n",
+        "\n",
+        "if index_name in pinecone.list_indexes():\n",
+        "   pinecone.delete_index(index_name)\n",
+        "\n",
+        "# we create a new index\n",
+        "pinecone.create_index(\n",
+        "    name=index_name,\n",
+        "    metric='dotproduct',\n",
+        "    dimension=1536  # 1536 dim of text-embedding-ada-002\n",
+        ")\n",
+        "\n",
+        "# wait for index to be initialized\n",
+        "while not pinecone.describe_index(index_name).status['ready']:\n",
+        "    time.sleep(1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uiSWrAQ5aRco"
+      },
+      "source": [
+        "Then connect to the index:"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -479,32 +643,38 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "K5Q16wRH9SmO",
-        "outputId": "fdf2947f-a270-4417-a02b-057e62356dfe"
+        "id": "bfsfuFmqaS4G",
+        "outputId": "45f17443-b87a-4682-ab44-6cfd6efdc46c"
       },
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "18891"
+              "{'dimension': 1536,\n",
+              " 'index_fullness': 0.0,\n",
+              " 'namespaces': {},\n",
+              " 'total_vector_count': 0}"
             ]
           },
+          "execution_count": 12,
           "metadata": {},
-          "execution_count": 3
+          "output_type": "execute_result"
         }
       ],
       "source": [
-        "len(dataset)"
+        "index = pinecone.GRPCIndex(index_name)\n",
+        "index.describe_index_stats()"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "c3-Plec39SmO"
+        "id": "QbDTrvvm9SmP"
       },
       "source": [
-        "We'll format the dataset ready for upsert and reduce what we use to a subset of the full dataset."
+        "We should see that the new Pinecone index has a `total_vector_count` of `0`, as we haven't added any vectors yet.\n",
+        "\n",
+        "Now we upsert the data to Pinecone:"
       ]
     },
     {
@@ -513,514 +683,73 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 206
+          "height": 98,
+          "referenced_widgets": [
+            "d7b2791e5f3d4c68b02da4123f715a72",
+            "e4e2a2e10c684ac7bbf102bad235464f",
+            "5290c01d786b4baf8b5e9adfe1a5befe",
+            "f073c54fdece48c0817f21f0970621d9",
+            "c593df22c7294a078a8d036d10e1c117",
+            "2a15af3253884c7cb97c6c6f3dd21e3f",
+            "bb3e80cb30214f6b80c4316606761d34",
+            "1a90f0bf4c8e4aabb8e346c3d9cfdff6",
+            "6e332262dd2944a68e3415bc827f1407",
+            "ded11ea7cc6b4c8aa6acbe8d03a6f742",
+            "bd0b82bd40b0418a8e326bec7e1cfe9e",
+            "2868c074bd55491a92000c7cd363ce6b",
+            "be04454d283147d79e353c1ab24b8573",
+            "89c1ae7c90004a6bae1e5aeedb19fa8c",
+            "b22dd946e0ac4a0abfb27efcf811c790",
+            "dd464906d8ab4900916b35dd3e779d46",
+            "4b2dd63f4b5e4a40ab5ec52826cc5bb3",
+            "87258691c1e041219045522dcf52bc52",
+            "9f125e0287ed46ebba34a0d26cdfb8cc",
+            "795dabbc25bb426c8756a36b5778f572",
+            "d9dd4543607840bfb4e813e801549c66",
+            "7e51d478a2e14ff09853d93c102ff40f"
+          ]
         },
-        "id": "4CW5mNi89SmO",
-        "outputId": "b7485e0d-1aa6-4f2b-840e-fdef19d58ffc"
+        "id": "AhDcbRGTaWPi",
+        "outputId": "14b0b058-fa02-4078-83b9-7c3067edf613"
       },
       "outputs": [
         {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "                         id  \\\n",
-              "0  5733be284776f41900661182   \n",
-              "1  5733bf84d058e614000b61be   \n",
-              "2  5733bed24776f41900661188   \n",
-              "3  5733a6424776f41900660f51   \n",
-              "4  5733a70c4776f41900660f64   \n",
-              "\n",
-              "                                              values  \\\n",
-              "0  [-0.010262451963272523, 0.02222637996192584, -...   \n",
-              "1  [-0.009786712423983223, -0.013988726438873078,...   \n",
-              "2  [0.013343917696606181, -0.0007001232846109822,...   \n",
-              "3  [-0.0085222901071539, 0.004399558219521822, -0...   \n",
-              "4  [-0.006695996885869355, -0.02067068565761649, ...   \n",
-              "\n",
-              "                                            metadata  \n",
-              "0  {'text': 'Architecturally, the school has a Ca...  \n",
-              "1  {'text': 'As at most other universities, Notre...  \n",
-              "2  {'text': 'The university is the major seat of ...  \n",
-              "3  {'text': 'The College of Engineering was estab...  \n",
-              "4  {'text': 'All of Notre Dame's undergraduate st...  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-ae493f02-d341-44ae-b022-ef7c62f7ae4d\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>id</th>\n",
-              "      <th>values</th>\n",
-              "      <th>metadata</th>\n",
-              "    </tr>\n",
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-              "      <td>5733be284776f41900661182</td>\n",
-              "      <td>[-0.010262451963272523, 0.02222637996192584, -...</td>\n",
-              "      <td>{'text': 'Architecturally, the school has a Ca...</td>\n",
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-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>5733bf84d058e614000b61be</td>\n",
-              "      <td>[-0.009786712423983223, -0.013988726438873078,...</td>\n",
-              "      <td>{'text': 'As at most other universities, Notre...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>5733bed24776f41900661188</td>\n",
-              "      <td>[0.013343917696606181, -0.0007001232846109822,...</td>\n",
-              "      <td>{'text': 'The university is the major seat of ...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
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-              "      <td>[-0.0085222901071539, 0.004399558219521822, -0...</td>\n",
-              "      <td>{'text': 'The College of Engineering was estab...</td>\n",
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-              "    <tr>\n",
-              "      <th>4</th>\n",
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-              "      <td>[-0.006695996885869355, -0.02067068565761649, ...</td>\n",
-              "      <td>{'text': 'All of Notre Dame's undergraduate st...</td>\n",
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-              "    <div class=\"colab-df-buttons\">\n",
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-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae493f02-d341-44ae-b022-ef7c62f7ae4d')\"\n",
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-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-ae493f02-d341-44ae-b022-ef7c62f7ae4d button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-ae493f02-d341-44ae-b022-ef7c62f7ae4d');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
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-              "\n",
-              "\n",
-              "<div id=\"df-2794b672-1cdf-41a1-8f1b-4d58ca5d2ecb\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2794b672-1cdf-41a1-8f1b-4d58ca5d2ecb')\"\n",
-              "            title=\"Suggest charts.\"\n",
-              "            style=\"display:none;\">\n",
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-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
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-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
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-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
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-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
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-              "\n",
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-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-2794b672-1cdf-41a1-8f1b-4d58ca5d2ecb button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 4
-        }
-      ],
-      "source": [
-        "# we drop sparse_values as they are not needed for this example\n",
-        "dataset.documents.drop(['sparse_values', 'blob'], axis=1, inplace=True)\n",
-        "\n",
-        "dataset.head()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "B2_Pt7N6Zg2X"
-      },
-      "source": [
-        "## Vector Database"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "JQTfOTR6aBRS"
-      },
-      "source": [
-        "Next we initialize the vector database. For this we need a [free API key](https://app.pinecone.io/), then we create the index:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "lgfywcQj9SmP"
-      },
-      "outputs": [],
-      "source": [
-        "index_name = 'langchain-retrieval-agent-fast'"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "C3wrG-9yaJel"
-      },
-      "outputs": [],
-      "source": [
-        "import pinecone\n",
-        "import os\n",
-        "\n",
-        "# Load Pinecone API key\n",
-        "api_key = os.getenv('PINECONE_API_KEY') or 'api_key'\n",
-        "# Set Pinecone environment. Find next to API key in console\n",
-        "env = os.getenv('PINECONE_ENVIRONMENT') or \"us-central1-gcp\"\n",
-        "\n",
-        "pinecone.init(api_key=api_key, environment=env)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "D5WT4PAN9SmP"
-      },
-      "outputs": [],
-      "source": [
-        "import time\n",
-        "\n",
-        "if index_name in pinecone.list_indexes():\n",
-        "   pinecone.delete_index(index_name)\n",
-        "\n",
-        "# we create a new index\n",
-        "pinecone.create_index(\n",
-        "    name=index_name,\n",
-        "    metric='dotproduct',\n",
-        "    dimension=1536  # 1536 dim of text-embedding-ada-002\n",
-        ")\n",
-        "\n",
-        "# wait for index to be initialized\n",
-        "while not pinecone.describe_index(index_name).status['ready']:\n",
-        "    time.sleep(1)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "uiSWrAQ5aRco"
-      },
-      "source": [
-        "Then connect to the index:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "bfsfuFmqaS4G",
-        "outputId": "45f17443-b87a-4682-ab44-6cfd6efdc46c"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "{'dimension': 1536,\n",
-              " 'index_fullness': 0.0,\n",
-              " 'namespaces': {},\n",
-              " 'total_vector_count': 0}"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 12
-        }
-      ],
-      "source": [
-        "index = pinecone.GRPCIndex(index_name)\n",
-        "index.describe_index_stats()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "QbDTrvvm9SmP"
-      },
-      "source": [
-        "We should see that the new Pinecone index has a `total_vector_count` of `0`, as we haven't added any vectors yet.\n",
-        "\n",
-        "Now we upsert the data to Pinecone:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 98,
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-            "7e51d478a2e14ff09853d93c102ff40f"
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-        },
-        "id": "AhDcbRGTaWPi",
-        "outputId": "14b0b058-fa02-4078-83b9-7c3067edf613"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d7b2791e5f3d4c68b02da4123f715a72",
+              "version_major": 2,
+              "version_minor": 0
+            },
             "text/plain": [
               "sending upsert requests:   0%|          | 0/18891 [00:00<?, ?it/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
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-              "model_id": "d7b2791e5f3d4c68b02da4123f715a72"
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-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "collecting async responses:   0%|          | 0/148 [00:00<?, ?it/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "2868c074bd55491a92000c7cd363ce6b",
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-              "model_id": "2868c074bd55491a92000c7cd363ce6b"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "collecting async responses:   0%|          | 0/148 [00:00<?, ?it/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "upserted_count: 18891"
             ]
           },
+          "execution_count": 13,
           "metadata": {},
-          "execution_count": 13
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -1048,7 +777,6 @@
       },
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "{'dimension': 1536,\n",
@@ -1057,8 +785,9 @@
               " 'total_vector_count': 18891}"
             ]
           },
+          "execution_count": 14,
           "metadata": {},
-          "execution_count": 14
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -1143,7 +872,6 @@
       },
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "[Document(page_content='Episcopalians and Presbyterians, as well as other WASPs, tend to be considerably wealthier and better educated (having graduate and post-graduate degrees per capita) than most other religious groups in United States, and are disproportionately represented in the upper reaches of American business, law and politics, especially the Republican Party. Numbers of the most wealthy and affluent American families as the Vanderbilts and the Astors, Rockefeller, Du Pont, Roosevelt, Forbes, Whitneys, the Morgans and Harrimans are Mainline Protestant families.', metadata={'title': 'Protestantism'}),\n",
@@ -1153,8 +881,9 @@
               " Document(page_content='In the United States, two of the wealthiest nonprofit organizations are the Bill and Melinda Gates Foundation, which has an endowment of US$38 billion, and the Howard Hughes Medical Institute originally funded by Hughes Aircraft prior to divestiture, which has an endowment of approximately $14.8 billion. Outside the United States, another large NPO is the British Wellcome Trust, which is a \"charity\" by British usage. See: List of wealthiest foundations. Note that this assessment excludes universities, at least a few of which have assets in the tens of billions of dollars. For example; List of U.S. colleges and universities by endowment.', metadata={'title': 'Nonprofit_organization'})]"
             ]
           },
+          "execution_count": 22,
           "metadata": {},
-          "execution_count": 22
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -1247,17 +976,17 @@
       },
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "'Based on the provided context, Yale University is mentioned as having received significant donations from wealthy individuals and families, such as Elihu Yale, the Harkness family, the Beinecke family, John William Sterling, Payne Whitney, Joseph E. Sheffield, Paul Mellon, Charles B. G. Murphy, William K. Lanman, and the Yale Class of 1954. These donations suggest a strong presence of intergenerational wealth at Yale University. However, it is important to note that this information does not provide a comprehensive ranking of universities based on intergenerational wealth.'"
-            ],
             "application/vnd.google.colaboratory.intrinsic+json": {
               "type": "string"
-            }
+            },
+            "text/plain": [
+              "'Based on the provided context, Yale University is mentioned as having received significant donations from wealthy individuals and families, such as Elihu Yale, the Harkness family, the Beinecke family, John William Sterling, Payne Whitney, Joseph E. Sheffield, Paul Mellon, Charles B. G. Murphy, William K. Lanman, and the Yale Class of 1954. These donations suggest a strong presence of intergenerational wealth at Yale University. However, it is important to note that this information does not provide a comprehensive ranking of universities based on intergenerational wealth.'"
+            ]
           },
+          "execution_count": 24,
           "metadata": {},
-          "execution_count": 24
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -1364,8 +1093,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "\n",
             "\n",
@@ -1384,7 +1113,6 @@
           ]
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "{'input': 'What universities had the most intergenerational wealth?',\n",
@@ -1392,8 +1120,9 @@
               " 'output': 'Some universities in the United States with large endowments include Harvard University, Stanford University, and Princeton University.'}"
             ]
           },
+          "execution_count": 27,
           "metadata": {},
-          "execution_count": 27
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -1421,8 +1150,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "\n",
             "\n",
@@ -1436,7 +1165,6 @@
           ]
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "{'input': 'what is 2 * 7?',\n",
@@ -1445,8 +1173,9 @@
               " 'output': 'The product of 2 multiplied by 7 is 14.'}"
             ]
           },
+          "execution_count": 28,
           "metadata": {},
-          "execution_count": 28
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -1474,8 +1203,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "\n",
             "\n",
@@ -1494,7 +1223,6 @@
           ]
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "{'input': 'can you tell me some facts about legacy admissions?',\n",
@@ -1505,8 +1233,9 @@
               " 'output': \"Legacy admissions refer to the practice of giving preferential treatment to applicants who have family members who attended the university in question. This means that if a student's parent, grandparent, or sibling attended the university, they may have a higher chance of being admitted compared to other applicants with similar qualifications. Legacy admissions are one of the factors that can be taken into account in the holistic admissions process used by some universities in the United States.\"}"
             ]
           },
+          "execution_count": 29,
           "metadata": {},
-          "execution_count": 29
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -1525,8 +1254,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "\n",
             "\n",
@@ -1549,7 +1278,6 @@
           ]
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "{'input': 'Teach a class of 7th graders how legacy admissions ruin the playing field.',\n",
@@ -1562,8 +1290,9 @@
               " 'output': 'Legacy admissions can have a negative impact on the playing field in college admissions by potentially disadvantaging applicants from underrepresented or disadvantaged backgrounds. By giving preferential treatment to legacy applicants, universities may prioritize the continuation of a privileged class rather than promoting diversity and equal opportunity.'}"
             ]
           },
+          "execution_count": 30,
           "metadata": {},
-          "execution_count": 30
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -1605,13 +1334,14 @@
     }
   ],
   "metadata": {
+    "accelerator": "GPU",
     "colab": {
-      "provenance": [],
-      "gpuType": "T4",
       "collapsed_sections": [
         "bhWwrfbbVGOA"
       ],
-      "include_colab_link": true
+      "gpuType": "T4",
+      "include_colab_link": true,
+      "provenance": []
     },
     "kernelspec": {
       "display_name": "Python 3",
@@ -1627,102 +1357,14 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
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