(LLMs)Embedding,_Vector_databses_&_search.ipynb

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            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers<5.0.0,>=4.6.0->sentence-transformers>=2.2.2->chromadb==0.3.21)\n",
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            "Building wheels for collected packages: hnswlib, sentence-transformers\n",
            "  Building wheel for hnswlib (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for hnswlib: filename=hnswlib-0.7.0-cp310-cp310-linux_x86_64.whl size=2201762 sha256=db24f6d18f17661d5315ab15cd661af06f69f71c0379f0b539ae8f9390075eb0\n",
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            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125926 sha256=35209d46c515df8ca5afd69e7b6618d5cae47cdb31ccf00ce04889fdc04b58e7\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
            "Successfully built hnswlib sentence-transformers\n",
            "Installing collected packages: tokenizers, sentencepiece, safetensors, monotonic, faiss-cpu, zstandard, websockets, uvloop, requests, python-dotenv, lz4, httptools, hnswlib, h11, backoff, watchfiles, uvicorn, starlette, posthog, huggingface-hub, clickhouse-connect, transformers, fastapi, sentence-transformers, chromadb\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.27.1\n",
            "    Uninstalling requests-2.27.1:\n",
            "      Successfully uninstalled requests-2.27.1\n",
            "\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-colab 1.0.0 requires requests==2.27.1, but you have requests 2.31.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed backoff-2.2.1 chromadb-0.3.21 clickhouse-connect-0.6.8 faiss-cpu-1.7.4 fastapi-0.100.0 h11-0.14.0 hnswlib-0.7.0 httptools-0.6.0 huggingface-hub-0.16.4 lz4-4.3.2 monotonic-1.6 posthog-3.0.1 python-dotenv-1.0.0 requests-2.31.0 safetensors-0.3.1 sentence-transformers-2.2.2 sentencepiece-0.1.99 starlette-0.27.0 tokenizers-0.13.3 transformers-4.31.0 uvicorn-0.23.1 uvloop-0.17.0 watchfiles-0.19.0 websockets-11.0.3 zstandard-0.21.0\n"
          ]
        }
      ],
      "source": [
        "%pip install faiss-cpu==1.7.4 chromadb==0.3.21"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "pdf = pd.read_csv(f\"/content/labelled_newscatcher_dataset.csv\", sep=\";\")\n",
        "pdf[\"id\"] = pdf.index\n",
        "display(pdf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "P9ensLIA4CgF",
        "outputId": "7789e818-f90c-42c8-965a-8b977d45eaef"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
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              "                topic                                               link  \\\n",
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              "...               ...                                                ...   \n",
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              "  .colab-df-quickchart: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",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-4d21f8d0-d256-478f-bae0-928000401a74 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-edbf80ee-e043-4182-a811-ffe736ed1485 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-edbf80ee-e043-4182-a811-ffe736ed1485');\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",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import InputExample"
      ],
      "metadata": {
        "id": "4ncDme7Y7QjJ"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pdf_subset = pdf.head(1000)"
      ],
      "metadata": {
        "id": "y62j1udY7RAO"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def example_create_fn(doc1: pd.Series) -> InputExample:\n",
        "    \"\"\"\n",
        "    Helper function that outputs a sentence_transformer guid, label, and text\n",
        "    \"\"\"\n",
        "    return InputExample(texts=[doc1])\n",
        "\n",
        "faiss_train_examples = pdf_subset.apply(\n",
        "    lambda x: example_create_fn(x[\"title\"]), axis=1\n",
        ").tolist()"
      ],
      "metadata": {
        "id": "xlwoS8PK7Yo4"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer"
      ],
      "metadata": {
        "id": "42D2VCUL7jmn"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = SentenceTransformer(\n",
        "    \"all-MiniLM-L6-v2\"\n",
        ")  # Use a pre-cached model\n",
        "faiss_title_embedding = model.encode(pdf_subset.title.values.tolist())\n",
        "len(faiss_title_embedding), len(faiss_title_embedding[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482,
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        "outputId": "1b3e46f9-9b17-4230-dc50-11c79f520384"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
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              "Downloading (…)7e55de9125/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)5de9125/modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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              "model_id": "4814d63213af498895d96a6435efad18"
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          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 384)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Step 3: Saving embedding vectors to FAISS index\n",
        "# Below, we create the FAISS index object based on our embedding vectors, normalize vectors, and add these vectors to the FAISS index.\n"
      ],
      "metadata": {
        "id": "C6jkJbZM7--V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import faiss\n",
        "\n",
        "pdf_to_index = pdf_subset.set_index([\"id\"], drop=False)\n",
        "id_index = np.array(pdf_to_index.id.values).flatten().astype(\"int\")\n",
        "\n",
        "content_encoded_normalized = faiss_title_embedding.copy()\n",
        "faiss.normalize_L2(content_encoded_normalized)\n",
        "\n",
        "# Index1DMap translates search results to IDs: https://faiss.ai/cpp_api/file/IndexIDMap_8h.html#_CPPv4I0EN5faiss18IndexIDMapTemplateE\n",
        "# The IndexFlatIP below builds index\n",
        "index_content = faiss.IndexIDMap(faiss.IndexFlatIP(len(faiss_title_embedding[0])))\n",
        "index_content.add_with_ids(content_encoded_normalized, id_index)"
      ],
      "metadata": {
        "id": "t0Mtikix8NYw"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Step 4: Search for relevant documents\n",
        "\n",
        "# We define a search function below to first vectorize our query text, and then search for the vectors with the closest distance.\n"
      ],
      "metadata": {
        "id": "fk1Kf4Rd8T8m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def search_content(query, pdf_to_index, k=3):\n",
        "    query_vector = model.encode([query])\n",
        "    faiss.normalize_L2(query_vector)\n",
        "\n",
        "    # We set k to limit the number of vectors we want to return\n",
        "    top_k = index_content.search(query_vector, k)\n",
        "    ids = top_k[1][0].tolist()\n",
        "    similarities = top_k[0][0].tolist()\n",
        "    results = pdf_to_index.loc[ids]\n",
        "    results[\"similarities\"] = similarities\n",
        "    return results"
      ],
      "metadata": {
        "id": "nW045xPg8cAU"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "display(search_content(\"animal\", pdf_to_index))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "S4bYKxbD8ehZ",
        "outputId": "4d932007-61dd-4b88-84f7-091c3f0d306a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          topic                                               link  \\\n",
              "id                                                                   \n",
              "176  TECHNOLOGY  https://www.pushsquare.com/news/2020/08/random...   \n",
              "975      HEALTH  https://www.news-medical.net/news/20200813/Res...   \n",
              "99   TECHNOLOGY  https://www.gematsu.com/2020/08/ghostwire-toky...   \n",
              "\n",
              "               domain       published_date  \\\n",
              "id                                           \n",
              "176    pushsquare.com  2020-08-03 16:30:00   \n",
              "975  news-medical.net  2020-08-13 05:18:00   \n",
              "99        gematsu.com  2020-08-07 16:43:13   \n",
              "\n",
              "                                                 title lang   id  similarities  \n",
              "id                                                                              \n",
              "176  Random: You Can Pick Up and Pet Cats in Assass...   en  176      0.391902  \n",
              "975  Researchers explore social behavior of animals...   en  975      0.376784  \n",
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              "<style scoped>\n",
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              "</style>"
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            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_6279148845861481243 = autoviz.get_df('df_6279148845861481243')\n",
              "\n",
              "def value_plot(df, y, sort_ascending=False, figsize=(2, 1)):\n",
              "  from matplotlib import pyplot as plt\n",
              "  if sort_ascending:\n",
              "    df = df.sort_values(y).reset_index(drop=True)\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  df[y].plot(kind='line')\n",
              "  plt.title(y)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = value_plot(df_6279148845861481243, *['id'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-5d1642a3-11ba-4f66-99c6-857653e782be\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-5d1642a3-11ba-4f66-99c6-857653e782be\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_1965322672793417281 = autoviz.get_df('df_1965322672793417281')\n",
              "\n",
              "def value_plot(df, y, sort_ascending=False, figsize=(2, 1)):\n",
              "  from matplotlib import pyplot as plt\n",
              "  if sort_ascending:\n",
              "    df = df.sort_values(y).reset_index(drop=True)\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  df[y].plot(kind='line')\n",
              "  plt.title(y)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = value_plot(df_1965322672793417281, *['similarities'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-421344e3-d51d-4aa2-8103-0d954a775a6e\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-421344e3-d51d-4aa2-8103-0d954a775a6e\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7ec8f5abdf00>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_7551316149413205940 = autoviz.get_df('df_7551316149413205940')\n",
              "\n",
              "def histogram(df, colname, num_bins=20, figsize=(2, 1)):\n",
              "  from matplotlib import pyplot as plt\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  plt.hist(df[colname], bins=num_bins, histtype='stepfilled')\n",
              "  plt.ylabel('count')\n",
              "  plt.title(colname)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = histogram(df_7551316149413205940, *['id'], **{})\n",
              "chart"
            ],
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              "      <div class=\"colab-quickchart-chart-with-code\" id=\"chart-f96904d7-656e-4e44-9ae8-f241f4d2211c\">\n",
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-f96904d7-656e-4e44-9ae8-f241f4d2211c\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-f96904d7-656e-4e44-9ae8-f241f4d2211c\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_478464931664598614 = autoviz.get_df('df_478464931664598614')\n",
              "\n",
              "def histogram(df, colname, num_bins=20, figsize=(2, 1)):\n",
              "  from matplotlib import pyplot as plt\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  plt.hist(df[colname], bins=num_bins, histtype='stepfilled')\n",
              "  plt.ylabel('count')\n",
              "  plt.title(colname)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = histogram(df_478464931664598614, *['similarities'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-e877a8d4-5577-4e08-ac90-7e8722008a32\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-e877a8d4-5577-4e08-ac90-7e8722008a32\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7ec8f59a5de0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Categorical distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_3006885522228747273 = autoviz.get_df('df_3006885522228747273')\n",
              "\n",
              "def categorical_histogram(df, colname, figsize=(2, 1.2), mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  bars = df[colname].value_counts()\n",
              "  plt.barh(bars.index, bars.values, color=sns.palettes.mpl_palette(mpl_palette_name))\n",
              "  plt.title(colname)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = categorical_histogram(df_3006885522228747273, *['topic'], **{})\n",
              "chart"
            ],
            "text/html": [
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              "rkJggg==\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-93d8feb9-82fb-4696-8e02-5340349bbcd7\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-93d8feb9-82fb-4696-8e02-5340349bbcd7\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_2658447446357893494 = autoviz.get_df('df_2658447446357893494')\n",
              "\n",
              "def categorical_histogram(df, colname, figsize=(2, 1.2), mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  bars = df[colname].value_counts()\n",
              "  plt.barh(bars.index, bars.values, color=sns.palettes.mpl_palette(mpl_palette_name))\n",
              "  plt.title(colname)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = categorical_histogram(df_2658447446357893494, *['link'], **{})\n",
              "chart"
            ],
            "text/html": [
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-dad86509-7b21-4917-94f3-c20c356301a7\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-dad86509-7b21-4917-94f3-c20c356301a7\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_4578888624921006435 = autoviz.get_df('df_4578888624921006435')\n",
              "\n",
              "def categorical_histogram(df, colname, figsize=(2, 1.2), mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  bars = df[colname].value_counts()\n",
              "  plt.barh(bars.index, bars.values, color=sns.palettes.mpl_palette(mpl_palette_name))\n",
              "  plt.title(colname)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = categorical_histogram(df_4578888624921006435, *['domain'], **{})\n",
              "chart"
            ],
            "text/html": [
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              "QmCC\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-87535af3-daba-43f8-b5d9-fd4fb4f51762\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-87535af3-daba-43f8-b5d9-fd4fb4f51762\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_7083092945717214939 = autoviz.get_df('df_7083092945717214939')\n",
              "\n",
              "def categorical_histogram(df, colname, figsize=(2, 1.2), mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  _, ax = plt.subplots(figsize=figsize)\n",
              "  bars = df[colname].value_counts()\n",
              "  plt.barh(bars.index, bars.values, color=sns.palettes.mpl_palette(mpl_palette_name))\n",
              "  plt.title(colname)\n",
              "  ax.spines[['top', 'right',]].set_visible(False)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = categorical_histogram(df_7083092945717214939, *['title'], **{})\n",
              "chart"
            ],
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              "gg==\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-0f748ef6-5241-4617-8c2f-e6738d7cf562\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-0f748ef6-5241-4617-8c2f-e6738d7cf562\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7ec8f5619090>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">2-d distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_892373123886768621 = autoviz.get_df('df_892373123886768621')\n",
              "\n",
              "def scatter_plots(df, colname_pairs, scatter_plot_size=2.5, size=8, alpha=.6):\n",
              "  from matplotlib import pyplot as plt\n",
              "  plt.figure(figsize=(len(colname_pairs) * scatter_plot_size, scatter_plot_size))\n",
              "  for plot_i, (x_colname, y_colname) in enumerate(colname_pairs, start=1):\n",
              "    ax = plt.subplot(1, len(colname_pairs), plot_i)\n",
              "    ax.scatter(df[x_colname], df[y_colname], s=size, alpha=alpha)\n",
              "    plt.xlabel(x_colname)\n",
              "    plt.ylabel(y_colname)\n",
              "    ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = scatter_plots(df_892373123886768621, *[[['id', 'similarities']]], **{})\n",
              "chart"
            ],
            "text/html": [
              "      <div class=\"colab-quickchart-chart-with-code\" id=\"chart-afbbef12-d124-4b89-b820-2716743b4c12\">\n",
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-afbbef12-d124-4b89-b820-2716743b4c12\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-afbbef12-d124-4b89-b820-2716743b4c12\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7ec8f5cee7d0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">2-d categorical distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_5553351878634563869 = autoviz.get_df('df_5553351878634563869')\n",
              "\n",
              "def heatmap(df, x_colname, y_colname, figsize=(2, 2), mpl_palette_name='viridis'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  import pandas as pd\n",
              "  plt.subplots(figsize=figsize)\n",
              "  df_2dhist = pd.DataFrame({\n",
              "      x_label: grp[y_colname].value_counts()\n",
              "      for x_label, grp in df.groupby(x_colname)\n",
              "  })\n",
              "  sns.heatmap(df_2dhist, cmap=mpl_palette_name)\n",
              "  plt.xlabel(x_colname)\n",
              "  plt.ylabel(y_colname)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = heatmap(df_5553351878634563869, *['topic', 'link'], **{})\n",
              "chart"
            ],
            "text/html": [
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-bbe5a857-f9c1-4add-beea-f939e468cf2d\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-bbe5a857-f9c1-4add-beea-f939e468cf2d\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_4499774779790904965 = autoviz.get_df('df_4499774779790904965')\n",
              "\n",
              "def heatmap(df, x_colname, y_colname, figsize=(2, 2), mpl_palette_name='viridis'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  import pandas as pd\n",
              "  plt.subplots(figsize=figsize)\n",
              "  df_2dhist = pd.DataFrame({\n",
              "      x_label: grp[y_colname].value_counts()\n",
              "      for x_label, grp in df.groupby(x_colname)\n",
              "  })\n",
              "  sns.heatmap(df_2dhist, cmap=mpl_palette_name)\n",
              "  plt.xlabel(x_colname)\n",
              "  plt.ylabel(y_colname)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = heatmap(df_4499774779790904965, *['link', 'domain'], **{})\n",
              "chart"
            ],
            "text/html": [
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-a301e3ce-bae1-44a1-818d-8b230314bdec\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-a301e3ce-bae1-44a1-818d-8b230314bdec\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_7119729233029201769 = autoviz.get_df('df_7119729233029201769')\n",
              "\n",
              "def heatmap(df, x_colname, y_colname, figsize=(2, 2), mpl_palette_name='viridis'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  import pandas as pd\n",
              "  plt.subplots(figsize=figsize)\n",
              "  df_2dhist = pd.DataFrame({\n",
              "      x_label: grp[y_colname].value_counts()\n",
              "      for x_label, grp in df.groupby(x_colname)\n",
              "  })\n",
              "  sns.heatmap(df_2dhist, cmap=mpl_palette_name)\n",
              "  plt.xlabel(x_colname)\n",
              "  plt.ylabel(y_colname)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = heatmap(df_7119729233029201769, *['domain', 'title'], **{})\n",
              "chart"
            ],
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              "w8Jqb9kZRwAAAABJRU5ErkJggg==\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-a188856d-ef84-44e9-8f96-284e98c2d658\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-a188856d-ef84-44e9-8f96-284e98c2d658\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7ec8f5363a30>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Faceted distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_1327635552952412937 = autoviz.get_df('df_1327635552952412937')\n",
              "\n",
              "def violin_plot(df, value_colname, facet_colname, col_width=.3, col_length=3, mpl_palette_name='Dark2', **kwargs):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  plt.figure(figsize=(col_length, col_width * len(df[facet_colname].unique())))\n",
              "  sns.violinplot(df, x=value_colname, y=facet_colname, palette=mpl_palette_name, **kwargs)\n",
              "  sns.despine(top=True, right=True, bottom=True, left=True)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = violin_plot(df_1327635552952412937, *['id', 'topic'], **{'inner': 'stick'})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-29c5cd97-b7cd-443f-bfc8-2ee76399f25b\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-29c5cd97-b7cd-443f-bfc8-2ee76399f25b\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_16297543697307344 = autoviz.get_df('df_16297543697307344')\n",
              "\n",
              "def violin_plot(df, value_colname, facet_colname, col_width=.3, col_length=3, mpl_palette_name='Dark2', **kwargs):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  plt.figure(figsize=(col_length, col_width * len(df[facet_colname].unique())))\n",
              "  sns.violinplot(df, x=value_colname, y=facet_colname, palette=mpl_palette_name, **kwargs)\n",
              "  sns.despine(top=True, right=True, bottom=True, left=True)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = violin_plot(df_16297543697307344, *['id', 'link'], **{'inner': 'stick'})\n",
              "chart"
            ],
            "text/html": [
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-757c52ae-0b79-4f39-a34c-ec56a947f8a2\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-757c52ae-0b79-4f39-a34c-ec56a947f8a2\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_6162391225389820204 = autoviz.get_df('df_6162391225389820204')\n",
              "\n",
              "def violin_plot(df, value_colname, facet_colname, col_width=.3, col_length=3, mpl_palette_name='Dark2', **kwargs):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  plt.figure(figsize=(col_length, col_width * len(df[facet_colname].unique())))\n",
              "  sns.violinplot(df, x=value_colname, y=facet_colname, palette=mpl_palette_name, **kwargs)\n",
              "  sns.despine(top=True, right=True, bottom=True, left=True)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = violin_plot(df_6162391225389820204, *['id', 'domain'], **{'inner': 'stick'})\n",
              "chart"
            ],
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              "AAAAAElFTkSuQmCC\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-6342a1ef-6b5d-42cc-8e7b-ce2f65bcdfa9\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-6342a1ef-6b5d-42cc-8e7b-ce2f65bcdfa9\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_1557428661444599881 = autoviz.get_df('df_1557428661444599881')\n",
              "\n",
              "def violin_plot(df, value_colname, facet_colname, col_width=.3, col_length=3, mpl_palette_name='Dark2', **kwargs):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  plt.figure(figsize=(col_length, col_width * len(df[facet_colname].unique())))\n",
              "  sns.violinplot(df, x=value_colname, y=facet_colname, palette=mpl_palette_name, **kwargs)\n",
              "  sns.despine(top=True, right=True, bottom=True, left=True)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = violin_plot(df_1557428661444599881, *['id', 'title'], **{'inner': 'stick'})\n",
              "chart"
            ],
            "text/html": [
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-95af3cb5-dfd9-41f3-bfae-18412baf1ebb\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-95af3cb5-dfd9-41f3-bfae-18412baf1ebb\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7ec8f516ce50>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Time series</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_4997316086478250623 = autoviz.get_df('df_4997316086478250623')\n",
              "\n",
              "def time_series_multiline(df, timelike_colname, value_colname, series_colname, figsize=(2.5, 1.3), mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  palette = list(sns.palettes.mpl_palette(mpl_palette_name))\n",
              "  def _plot_series(series, series_name, series_index=0):\n",
              "    if value_colname == 'count()':\n",
              "      counted = (series[timelike_colname]\n",
              "                 .value_counts()\n",
              "                 .reset_index(name='counts')\n",
              "                 .rename({'index': timelike_colname}, axis=1)\n",
              "                 .sort_values(timelike_colname, ascending=True))\n",
              "      xs = counted[timelike_colname]\n",
              "      ys = counted['counts']\n",
              "    else:\n",
              "      xs = series[timelike_colname]\n",
              "      ys = series[value_colname]\n",
              "    plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "  fig, ax = plt.subplots(figsize=figsize, layout='constrained')\n",
              "  df = df.sort_values(timelike_colname, ascending=True)\n",
              "  if series_colname:\n",
              "    for i, (series_name, series) in enumerate(df.groupby(series_colname)):\n",
              "      _plot_series(series, series_name, i)\n",
              "    fig.legend(title=series_colname, bbox_to_anchor=(1, 1), loc='upper left')\n",
              "  else:\n",
              "    _plot_series(df, '')\n",
              "  sns.despine(fig=fig, ax=ax)\n",
              "  plt.xlabel(timelike_colname)\n",
              "  plt.ylabel(value_colname)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = time_series_multiline(df_4997316086478250623, *['published_date', 'id', 'topic'], **{})\n",
              "chart"
            ],
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              "H6PK2nzsjBzLAAAAAElFTkSuQmCC\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-a4852f1a-2a59-4b15-a957-b5c60ce413b0\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-a4852f1a-2a59-4b15-a957-b5c60ce413b0\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_4256731462846138368 = autoviz.get_df('df_4256731462846138368')\n",
              "\n",
              "def time_series_multiline(df, timelike_colname, value_colname, series_colname, figsize=(2.5, 1.3), mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  palette = list(sns.palettes.mpl_palette(mpl_palette_name))\n",
              "  def _plot_series(series, series_name, series_index=0):\n",
              "    if value_colname == 'count()':\n",
              "      counted = (series[timelike_colname]\n",
              "                 .value_counts()\n",
              "                 .reset_index(name='counts')\n",
              "                 .rename({'index': timelike_colname}, axis=1)\n",
              "                 .sort_values(timelike_colname, ascending=True))\n",
              "      xs = counted[timelike_colname]\n",
              "      ys = counted['counts']\n",
              "    else:\n",
              "      xs = series[timelike_colname]\n",
              "      ys = series[value_colname]\n",
              "    plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "  fig, ax = plt.subplots(figsize=figsize, layout='constrained')\n",
              "  df = df.sort_values(timelike_colname, ascending=True)\n",
              "  if series_colname:\n",
              "    for i, (series_name, series) in enumerate(df.groupby(series_colname)):\n",
              "      _plot_series(series, series_name, i)\n",
              "    fig.legend(title=series_colname, bbox_to_anchor=(1, 1), loc='upper left')\n",
              "  else:\n",
              "    _plot_series(df, '')\n",
              "  sns.despine(fig=fig, ax=ax)\n",
              "  plt.xlabel(timelike_colname)\n",
              "  plt.ylabel(value_colname)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = time_series_multiline(df_4256731462846138368, *['published_date', 'id', 'link'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-b37ed614-22e4-45da-83f2-e40bf117f6b8\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-b37ed614-22e4-45da-83f2-e40bf117f6b8\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_3404674123293219989 = autoviz.get_df('df_3404674123293219989')\n",
              "\n",
              "def time_series_multiline(df, timelike_colname, value_colname, series_colname, figsize=(2.5, 1.3), mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  palette = list(sns.palettes.mpl_palette(mpl_palette_name))\n",
              "  def _plot_series(series, series_name, series_index=0):\n",
              "    if value_colname == 'count()':\n",
              "      counted = (series[timelike_colname]\n",
              "                 .value_counts()\n",
              "                 .reset_index(name='counts')\n",
              "                 .rename({'index': timelike_colname}, axis=1)\n",
              "                 .sort_values(timelike_colname, ascending=True))\n",
              "      xs = counted[timelike_colname]\n",
              "      ys = counted['counts']\n",
              "    else:\n",
              "      xs = series[timelike_colname]\n",
              "      ys = series[value_colname]\n",
              "    plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "  fig, ax = plt.subplots(figsize=figsize, layout='constrained')\n",
              "  df = df.sort_values(timelike_colname, ascending=True)\n",
              "  if series_colname:\n",
              "    for i, (series_name, series) in enumerate(df.groupby(series_colname)):\n",
              "      _plot_series(series, series_name, i)\n",
              "    fig.legend(title=series_colname, bbox_to_anchor=(1, 1), loc='upper left')\n",
              "  else:\n",
              "    _plot_series(df, '')\n",
              "  sns.despine(fig=fig, ax=ax)\n",
              "  plt.xlabel(timelike_colname)\n",
              "  plt.ylabel(value_colname)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = time_series_multiline(df_3404674123293219989, *['published_date', 'id', 'domain'], **{})\n",
              "chart"
            ],
            "text/html": [
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              "7ch7YAAAAABJRU5ErkJggg==\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-73005708-e85e-4633-8420-421c4852eaa3\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-73005708-e85e-4633-8420-421c4852eaa3\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "df_2933858137670343035 = autoviz.get_df('df_2933858137670343035')\n",
              "\n",
              "def time_series_multiline(df, timelike_colname, value_colname, series_colname, figsize=(2.5, 1.3), mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  palette = list(sns.palettes.mpl_palette(mpl_palette_name))\n",
              "  def _plot_series(series, series_name, series_index=0):\n",
              "    if value_colname == 'count()':\n",
              "      counted = (series[timelike_colname]\n",
              "                 .value_counts()\n",
              "                 .reset_index(name='counts')\n",
              "                 .rename({'index': timelike_colname}, axis=1)\n",
              "                 .sort_values(timelike_colname, ascending=True))\n",
              "      xs = counted[timelike_colname]\n",
              "      ys = counted['counts']\n",
              "    else:\n",
              "      xs = series[timelike_colname]\n",
              "      ys = series[value_colname]\n",
              "    plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "  fig, ax = plt.subplots(figsize=figsize, layout='constrained')\n",
              "  df = df.sort_values(timelike_colname, ascending=True)\n",
              "  if series_colname:\n",
              "    for i, (series_name, series) in enumerate(df.groupby(series_colname)):\n",
              "      _plot_series(series, series_name, i)\n",
              "    fig.legend(title=series_colname, bbox_to_anchor=(1, 1), loc='upper left')\n",
              "  else:\n",
              "    _plot_series(df, '')\n",
              "  sns.despine(fig=fig, ax=ax)\n",
              "  plt.xlabel(timelike_colname)\n",
              "  plt.ylabel(value_colname)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = time_series_multiline(df_2933858137670343035, *['published_date', 'id', 'title'], **{})\n",
              "chart"
            ],
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              "K+44MMYYY4wxxmrFHQfGGGOMMcZYrf4/Xo6yTulxbBIAAAAASUVORK5CYII=\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-e605e0ee-e325-4c97-b5a4-a7ae831c1577\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-e605e0ee-e325-4c97-b5a4-a7ae831c1577\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install chromadb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kgd5xZIr84x4",
        "outputId": "c383afd4-b344-49e2-90eb-5f41c02c5ea7"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: chromadb in /usr/local/lib/python3.10/dist-packages (0.3.21)\n",
            "Requirement already satisfied: pandas>=1.3 in /usr/local/lib/python3.10/dist-packages (from chromadb) (1.5.3)\n",
            "Requirement already satisfied: requests>=2.28 in /usr/local/lib/python3.10/dist-packages (from chromadb) (2.31.0)\n",
            "Requirement already satisfied: pydantic>=1.9 in /usr/local/lib/python3.10/dist-packages (from chromadb) (1.10.11)\n",
            "Requirement already satisfied: hnswlib>=0.7 in /usr/local/lib/python3.10/dist-packages (from chromadb) (0.7.0)\n",
            "Requirement already satisfied: clickhouse-connect>=0.5.7 in /usr/local/lib/python3.10/dist-packages (from chromadb) (0.6.8)\n",
            "Requirement already satisfied: sentence-transformers>=2.2.2 in /usr/local/lib/python3.10/dist-packages (from chromadb) (2.2.2)\n",
            "Requirement already satisfied: duckdb>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from chromadb) (0.8.1)\n",
            "Requirement already satisfied: fastapi>=0.85.1 in /usr/local/lib/python3.10/dist-packages (from chromadb) (0.100.0)\n",
            "Requirement already satisfied: uvicorn[standard]>=0.18.3 in /usr/local/lib/python3.10/dist-packages (from chromadb) (0.23.1)\n",
            "Requirement already satisfied: numpy>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from chromadb) (1.22.4)\n",
            "Requirement already satisfied: posthog>=2.4.0 in /usr/local/lib/python3.10/dist-packages (from chromadb) (3.0.1)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from clickhouse-connect>=0.5.7->chromadb) (2023.5.7)\n",
            "Requirement already satisfied: importlib-metadata in /usr/lib/python3/dist-packages (from clickhouse-connect>=0.5.7->chromadb) (4.6.4)\n",
            "Requirement already satisfied: urllib3>=1.26 in /usr/local/lib/python3.10/dist-packages (from clickhouse-connect>=0.5.7->chromadb) (1.26.16)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.10/dist-packages (from clickhouse-connect>=0.5.7->chromadb) (2022.7.1)\n",
            "Requirement already satisfied: zstandard in /usr/local/lib/python3.10/dist-packages (from clickhouse-connect>=0.5.7->chromadb) (0.21.0)\n",
            "Requirement already satisfied: lz4 in /usr/local/lib/python3.10/dist-packages (from clickhouse-connect>=0.5.7->chromadb) (4.3.2)\n",
            "Requirement already satisfied: starlette<0.28.0,>=0.27.0 in /usr/local/lib/python3.10/dist-packages (from fastapi>=0.85.1->chromadb) (0.27.0)\n",
            "Requirement already satisfied: typing-extensions>=4.5.0 in /usr/local/lib/python3.10/dist-packages (from fastapi>=0.85.1->chromadb) (4.7.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.3->chromadb) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from posthog>=2.4.0->chromadb) (1.16.0)\n",
            "Requirement already satisfied: monotonic>=1.5 in /usr/local/lib/python3.10/dist-packages (from posthog>=2.4.0->chromadb) (1.6)\n",
            "Requirement already satisfied: backoff>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from posthog>=2.4.0->chromadb) (2.2.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.28->chromadb) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.28->chromadb) (3.4)\n",
            "Requirement already satisfied: transformers<5.0.0,>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=2.2.2->chromadb) (4.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=2.2.2->chromadb) (4.65.0)\n",
            "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=2.2.2->chromadb) (2.0.1+cu118)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=2.2.2->chromadb) (0.15.2+cu118)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=2.2.2->chromadb) (1.2.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=2.2.2->chromadb) (1.10.1)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=2.2.2->chromadb) (3.8.1)\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=2.2.2->chromadb) (0.1.99)\n",
            "Requirement already satisfied: huggingface-hub>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=2.2.2->chromadb) (0.16.4)\n",
            "Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.10/dist-packages (from uvicorn[standard]>=0.18.3->chromadb) (8.1.6)\n",
            "Requirement already satisfied: h11>=0.8 in /usr/local/lib/python3.10/dist-packages (from uvicorn[standard]>=0.18.3->chromadb) (0.14.0)\n",
            "Requirement already satisfied: httptools>=0.5.0 in /usr/local/lib/python3.10/dist-packages (from uvicorn[standard]>=0.18.3->chromadb) (0.6.0)\n",
            "Requirement already satisfied: python-dotenv>=0.13 in /usr/local/lib/python3.10/dist-packages (from uvicorn[standard]>=0.18.3->chromadb) (1.0.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from uvicorn[standard]>=0.18.3->chromadb) (6.0.1)\n",
            "Requirement already satisfied: uvloop!=0.15.0,!=0.15.1,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from uvicorn[standard]>=0.18.3->chromadb) (0.17.0)\n",
            "Requirement already satisfied: watchfiles>=0.13 in /usr/local/lib/python3.10/dist-packages (from uvicorn[standard]>=0.18.3->chromadb) (0.19.0)\n",
            "Requirement already satisfied: websockets>=10.4 in /usr/local/lib/python3.10/dist-packages (from uvicorn[standard]>=0.18.3->chromadb) (11.0.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=2.2.2->chromadb) (3.12.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=2.2.2->chromadb) (2023.6.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=2.2.2->chromadb) (23.1)\n",
            "Requirement already satisfied: anyio<5,>=3.4.0 in /usr/local/lib/python3.10/dist-packages (from starlette<0.28.0,>=0.27.0->fastapi>=0.85.1->chromadb) (3.7.1)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence-transformers>=2.2.2->chromadb) (1.11.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence-transformers>=2.2.2->chromadb) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence-transformers>=2.2.2->chromadb) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence-transformers>=2.2.2->chromadb) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.6.0->sentence-transformers>=2.2.2->chromadb) (3.25.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.6.0->sentence-transformers>=2.2.2->chromadb) (16.0.6)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers>=2.2.2->chromadb) (2022.10.31)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers>=2.2.2->chromadb) (0.13.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers>=2.2.2->chromadb) (0.3.1)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk->sentence-transformers>=2.2.2->chromadb) (1.3.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers>=2.2.2->chromadb) (3.2.0)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision->sentence-transformers>=2.2.2->chromadb) (8.4.0)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.4.0->starlette<0.28.0,>=0.27.0->fastapi>=0.85.1->chromadb) (1.3.0)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.4.0->starlette<0.28.0,>=0.27.0->fastapi>=0.85.1->chromadb) (1.1.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.6.0->sentence-transformers>=2.2.2->chromadb) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.6.0->sentence-transformers>=2.2.2->chromadb) (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import chromadb\n",
        "from chromadb.config import Settings\n",
        "\n",
        "chroma_client = chromadb.Client()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sg1OaeHr8g68",
        "outputId": "757c3c39-448a-44f2-e954-45ceaa883c56"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:chromadb:Using embedded DuckDB without persistence: data will be transient\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "collection_name = \"my_news\""
      ],
      "metadata": {
        "id": "dTJBihPF9PHn"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if len(chroma_client.list_collections()) > 0 and collection_name in [chroma_client.list_collections()[0].name]:\n",
        "    chroma_client.delete_collection(name=collection_name)\n",
        "\n",
        "print(f\"Creating collection: '{collection_name}'\")\n",
        "collection = chroma_client.create_collection(name=collection_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "an2mnIK082Hm",
        "outputId": "392608a5-69c4-485e-a328-efae3b0d8989"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:chromadb.api.models.Collection:No embedding_function provided, using default embedding function: SentenceTransformerEmbeddingFunction\n",
            "Exception ignored in: <function PersistentDuckDB.__del__ at 0x7ec8f4690700>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/chromadb/db/duckdb.py\", line 446, in __del__\n",
            "    self.persist()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/chromadb/db/duckdb.py\", line 392, in persist\n",
            "    if not os.path.exists(self._save_folder):\n",
            "  File \"/usr/lib/python3.10/genericpath.py\", line 19, in exists\n",
            "    os.stat(path)\n",
            "TypeError: stat: path should be string, bytes, os.PathLike or integer, not NoneType\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Creating collection: 'my_news'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(pdf_subset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "inW1ETnn9LZQ",
        "outputId": "28ba5a5e-a424-49ef-c71c-8d5d158aa286"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          topic                                               link  \\\n",
              "0       SCIENCE  https://www.eurekalert.org/pub_releases/2020-0...   \n",
              "1       SCIENCE  https://www.pulse.ng/news/world/an-irresistibl...   \n",
              "2       SCIENCE  https://www.express.co.uk/news/science/1322607...   \n",
              "3       SCIENCE  https://www.ndtv.com/world-news/glaciers-could...   \n",
              "4       SCIENCE  https://www.thesun.ie/tech/5742187/perseid-met...   \n",
              "..          ...                                                ...   \n",
              "995  TECHNOLOGY  https://www.androidcentral.com/mate-40-will-be...   \n",
              "996     SCIENCE  https://www.cnn.com/2020/08/17/africa/stone-ag...   \n",
              "997      HEALTH  https://www.tenterfieldstar.com.au/story/68776...   \n",
              "998      HEALTH  https://news.sky.com/story/coronavirus-trials-...   \n",
              "999      HEALTH  https://www.techexplorist.com/study-demonstrat...   \n",
              "\n",
              "                     domain       published_date  \\\n",
              "0            eurekalert.org  2020-08-06 13:59:45   \n",
              "1                  pulse.ng  2020-08-12 15:14:19   \n",
              "2             express.co.uk  2020-08-13 21:01:00   \n",
              "3                  ndtv.com  2020-08-03 22:18:26   \n",
              "4                 thesun.ie  2020-08-12 19:54:36   \n",
              "..                      ...                  ...   \n",
              "995      androidcentral.com  2020-08-07 17:12:33   \n",
              "996                 cnn.com  2020-08-17 17:10:00   \n",
              "997  tenterfieldstar.com.au  2020-08-13 03:26:06   \n",
              "998            news.sky.com  2020-08-13 13:22:58   \n",
              "999       techexplorist.com  2020-08-10 07:47:00   \n",
              "\n",
              "                                                 title lang   id  \n",
              "0    A closer look at water-splitting's solar fuel ...   en    0  \n",
              "1    An irresistible scent makes locusts swarm, stu...   en    1  \n",
              "2    Artificial intelligence warning: AI will know ...   en    2  \n",
              "3     Glaciers Could Have Sculpted Mars Valleys: Study   en    3  \n",
              "4    Perseid meteor shower 2020: What time and how ...   en    4  \n",
              "..                                                 ...  ...  ...  \n",
              "995  The Mate 40 will be the last Huawei phone with...   en  995  \n",
              "996  Early humans knew how to make comfy, pest-free...   en  996  \n",
              "997           Regional Vic set for virus testing blitz   en  997  \n",
              "998  Coronavirus: Trials of second contact-tracing ...   en  998  \n",
              "999  The study demonstrates new treatment for prion...   en  999  \n",
              "\n",
              "[1000 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-4c3b925f-873a-4bb2-8e04-03bee38929d1\">\n",
              "    <div 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",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>topic</th>\n",
              "      <th>link</th>\n",
              "      <th>domain</th>\n",
              "      <th>published_date</th>\n",
              "      <th>title</th>\n",
              "      <th>lang</th>\n",
              "      <th>id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SCIENCE</td>\n",
              "      <td>https://www.eurekalert.org/pub_releases/2020-0...</td>\n",
              "      <td>eurekalert.org</td>\n",
              "      <td>2020-08-06 13:59:45</td>\n",
              "      <td>A closer look at water-splitting's solar fuel ...</td>\n",
              "      <td>en</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SCIENCE</td>\n",
              "      <td>https://www.pulse.ng/news/world/an-irresistibl...</td>\n",
              "      <td>pulse.ng</td>\n",
              "      <td>2020-08-12 15:14:19</td>\n",
              "      <td>An irresistible scent makes locusts swarm, stu...</td>\n",
              "      <td>en</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SCIENCE</td>\n",
              "      <td>https://www.express.co.uk/news/science/1322607...</td>\n",
              "      <td>express.co.uk</td>\n",
              "      <td>2020-08-13 21:01:00</td>\n",
              "      <td>Artificial intelligence warning: AI will know ...</td>\n",
              "      <td>en</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SCIENCE</td>\n",
              "      <td>https://www.ndtv.com/world-news/glaciers-could...</td>\n",
              "      <td>ndtv.com</td>\n",
              "      <td>2020-08-03 22:18:26</td>\n",
              "      <td>Glaciers Could Have Sculpted Mars Valleys: Study</td>\n",
              "      <td>en</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SCIENCE</td>\n",
              "      <td>https://www.thesun.ie/tech/5742187/perseid-met...</td>\n",
              "      <td>thesun.ie</td>\n",
              "      <td>2020-08-12 19:54:36</td>\n",
              "      <td>Perseid meteor shower 2020: What time and how ...</td>\n",
              "      <td>en</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>TECHNOLOGY</td>\n",
              "      <td>https://www.androidcentral.com/mate-40-will-be...</td>\n",
              "      <td>androidcentral.com</td>\n",
              "      <td>2020-08-07 17:12:33</td>\n",
              "      <td>The Mate 40 will be the last Huawei phone with...</td>\n",
              "      <td>en</td>\n",
              "      <td>995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>SCIENCE</td>\n",
              "      <td>https://www.cnn.com/2020/08/17/africa/stone-ag...</td>\n",
              "      <td>cnn.com</td>\n",
              "      <td>2020-08-17 17:10:00</td>\n",
              "      <td>Early humans knew how to make comfy, pest-free...</td>\n",
              "      <td>en</td>\n",
              "      <td>996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>HEALTH</td>\n",
              "      <td>https://www.tenterfieldstar.com.au/story/68776...</td>\n",
              "      <td>tenterfieldstar.com.au</td>\n",
              "      <td>2020-08-13 03:26:06</td>\n",
              "      <td>Regional Vic set for virus testing blitz</td>\n",
              "      <td>en</td>\n",
              "      <td>997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>HEALTH</td>\n",
              "      <td>https://news.sky.com/story/coronavirus-trials-...</td>\n",
              "      <td>news.sky.com</td>\n",
              "      <td>2020-08-13 13:22:58</td>\n",
              "      <td>Coronavirus: Trials of second contact-tracing ...</td>\n",
              "      <td>en</td>\n",
              "      <td>998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>HEALTH</td>\n",
              "      <td>https://www.techexplorist.com/study-demonstrat...</td>\n",
              "      <td>techexplorist.com</td>\n",
              "      <td>2020-08-10 07:47:00</td>\n",
              "      <td>The study demonstrates new treatment for prion...</td>\n",
              "      <td>en</td>\n",
              "      <td>999</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4c3b925f-873a-4bb2-8e04-03bee38929d1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-5fae588e-7c65-4de8-9b0b-37e5986dbfa5\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5fae588e-7c65-4de8-9b0b-37e5986dbfa5')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-5fae588e-7c65-4de8-9b0b-37e5986dbfa5 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-4c3b925f-873a-4bb2-8e04-03bee38929d1 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-4c3b925f-873a-4bb2-8e04-03bee38929d1');\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",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "collection.add(\n",
        "    documents=pdf_subset[\"title\"][:100].tolist(),\n",
        "    metadatas=[{\"topic\": topic} for topic in pdf_subset[\"topic\"][:100].tolist()],\n",
        "    ids=[f\"id{x}\" for x in range(100)],\n",
        ")\n"
      ],
      "metadata": {
        "id": "5n5_6cp89TpI"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "results = collection.query(query_texts=[\"space\"], n_results=10)\n",
        "\n",
        "print(json.dumps(results, indent=4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Cr2-nH19W8L",
        "outputId": "5bbc5eaf-7c5d-465a-f723-f5ae82ff0e95"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "    \"ids\": [\n",
            "        [\n",
            "            \"id72\",\n",
            "            \"id7\",\n",
            "            \"id30\",\n",
            "            \"id26\",\n",
            "            \"id23\",\n",
            "            \"id76\",\n",
            "            \"id69\",\n",
            "            \"id40\",\n",
            "            \"id47\",\n",
            "            \"id75\"\n",
            "        ]\n",
            "    ],\n",
            "    \"embeddings\": null,\n",
            "    \"documents\": [\n",
            "        [\n",
            "            \"Beck teams up with NASA and AI for 'Hyperspace' visual album experience\",\n",
            "            \"Orbital space tourism set for rebirth in 2021\",\n",
            "            \"NASA drops \\\"insensitive\\\" nicknames for cosmic objects\",\n",
            "            \"\\u2018It came alive:\\u2019 NASA astronauts describe experiencing splashdown in SpaceX Dragon\",\n",
            "            \"Hubble Uses Moon As \\u201cMirror\\u201d to Study Earth\\u2019s Atmosphere \\u2013 Proxy in Search of Potentially Habitable Planets Around Other Stars\",\n",
            "            \"Australia's small yet crucial part in the mission to find life on Mars\",\n",
            "            \"NASA Astronauts in SpaceX Capsule Splashdown in Gulf Of Mexico\",\n",
            "            \"SpaceX's Starship spacecraft saw 150 meters high\",\n",
            "            \"NASA\\u2019s InSight lander shows what\\u2019s beneath Mars\\u2019 surface\",\n",
            "            \"Alien base on Mercury: ET hunters claim to find huge UFO\"\n",
            "        ]\n",
            "    ],\n",
            "    \"metadatas\": [\n",
            "        [\n",
            "            {\n",
            "                \"topic\": \"TECHNOLOGY\"\n",
            "            },\n",
            "            {\n",
            "                \"topic\": \"SCIENCE\"\n",
            "            },\n",
            "            {\n",
            "                \"topic\": \"SCIENCE\"\n",
            "            },\n",
            "            {\n",
            "                \"topic\": \"SCIENCE\"\n",
            "            },\n",
            "            {\n",
            "                \"topic\": \"SCIENCE\"\n",
            "            },\n",
            "            {\n",
            "                \"topic\": \"SCIENCE\"\n",
            "            },\n",
            "            {\n",
            "                \"topic\": \"SCIENCE\"\n",
            "            },\n",
            "            {\n",
            "                \"topic\": \"SCIENCE\"\n",
            "            },\n",
            "            {\n",
            "                \"topic\": \"SCIENCE\"\n",
            "            },\n",
            "            {\n",
            "                \"topic\": \"SCIENCE\"\n",
            "            }\n",
            "        ]\n",
            "    ],\n",
            "    \"distances\": [\n",
            "        [\n",
            "            1.2250354290008545,\n",
            "            1.3089773654937744,\n",
            "            1.391038417816162,\n",
            "            1.4064621925354004,\n",
            "            1.4391300678253174,\n",
            "            1.4898791313171387,\n",
            "            1.5728247165679932,\n",
            "            1.5738130807876587,\n",
            "            1.5835301876068115,\n",
            "            1.5864628553390503\n",
            "        ]\n",
            "    ]\n",
            "}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "collection.query(query_texts=[\"space\"], where={\"topic\": \"SCIENCE\"}, n_results=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OVDyh0GX9cz2",
        "outputId": "da91692b-895c-40b8-9b17-61e45df03122"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ids': [['id7',\n",
              "   'id30',\n",
              "   'id26',\n",
              "   'id23',\n",
              "   'id76',\n",
              "   'id69',\n",
              "   'id40',\n",
              "   'id47',\n",
              "   'id75',\n",
              "   'id52']],\n",
              " 'embeddings': None,\n",
              " 'documents': [['Orbital space tourism set for rebirth in 2021',\n",
              "   'NASA drops \"insensitive\" nicknames for cosmic objects',\n",
              "   '‘It came alive:’ NASA astronauts describe experiencing splashdown in SpaceX Dragon',\n",
              "   'Hubble Uses Moon As “Mirror” to Study Earth’s Atmosphere – Proxy in Search of Potentially Habitable Planets Around Other Stars',\n",
              "   \"Australia's small yet crucial part in the mission to find life on Mars\",\n",
              "   'NASA Astronauts in SpaceX Capsule Splashdown in Gulf Of Mexico',\n",
              "   \"SpaceX's Starship spacecraft saw 150 meters high\",\n",
              "   'NASA’s InSight lander shows what’s beneath Mars’ surface',\n",
              "   'Alien base on Mercury: ET hunters claim to find huge UFO',\n",
              "   'SpaceX Crew-1 mission with NASA, first fully operational crewed mission to space to launch in October']],\n",
              " 'metadatas': [[{'topic': 'SCIENCE'},\n",
              "   {'topic': 'SCIENCE'},\n",
              "   {'topic': 'SCIENCE'},\n",
              "   {'topic': 'SCIENCE'},\n",
              "   {'topic': 'SCIENCE'},\n",
              "   {'topic': 'SCIENCE'},\n",
              "   {'topic': 'SCIENCE'},\n",
              "   {'topic': 'SCIENCE'},\n",
              "   {'topic': 'SCIENCE'},\n",
              "   {'topic': 'SCIENCE'}]],\n",
              " 'distances': [[1.3089773654937744,\n",
              "   1.391038417816162,\n",
              "   1.4064621925354004,\n",
              "   1.4391300678253174,\n",
              "   1.4898791313171387,\n",
              "   1.5728247165679932,\n",
              "   1.5738130807876587,\n",
              "   1.5835301876068115,\n",
              "   1.5864628553390503,\n",
              "   1.5984288454055786]]}"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "collection.delete(ids=[\"id0\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N-b8wUsa9i8N",
        "outputId": "7e28d6dc-003b-42e0-dc47-0e057c41f647"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[UUID('f05f530f-d08a-4172-8ceb-d25cfc6a2dbd')]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "collection.get(\n",
        "    ids=[\"id0\"],\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5aClULUy9pHI",
        "outputId": "19231c36-ba16-47c0-95b3-72a25e102c5c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ids': [], 'embeddings': None, 'documents': [], 'metadatas': []}"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "collection.get(\n",
        "    ids=[\"id2\"],\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9_TsdFm29sQs",
        "outputId": "5b2fa72d-334b-4f66-ceaf-ca7925152ddf"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ids': ['id2'],\n",
              " 'embeddings': None,\n",
              " 'documents': ['Artificial intelligence warning: AI will know us better than we know ourselves'],\n",
              " 'metadatas': [{'topic': 'SCIENCE'}]}"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "collection.update(\n",
        "    ids=[\"id2\"],\n",
        "    metadatas=[{\"topic\": \"TECHNOLOGY\"}],\n",
        ")"
      ],
      "metadata": {
        "id": "rjuUZnxE9vSm"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline\n",
        "\n",
        "model_id = \"gpt2\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "lm_model = AutoModelForCausalLM.from_pretrained(model_id)\n",
        "\n",
        "pipe = pipeline(\n",
        "    \"text-generation\",\n",
        "    model=lm_model,\n",
        "    tokenizer=tokenizer,\n",
        "    max_new_tokens=512,\n",
        "    device_map=\"auto\",\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420,
          "referenced_widgets": [
            "5e43f1555c324656bd32a8db184e1bc1",
            "861096ac1fe748718192c69f54105534",
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            "f7280eed80d44dc2b84510c5ea19b688",
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            "1a1e7a6ef2ee4fb0b4d56eeeb0df3031",
            "7afae6ac67dd4479bbe743b8c08402ea",
            "c86f97c0315f4d03a1595382b954322a",
            "9c831b7b1d5348b5b16672564609dac3",
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            "4f001fcae7a9405996f9e78bf74936b1",
            "4386641ae21447b8be902535b9e5415d",
            "1a1ff9dcf93b458bb34059c989fb6e5b",
            "8797b3383abb4a348587e643841467d6",
            "de82a25a7c68481c883cd94946acab17",
            "69789ace25cb49a8bf6e3e97410fb0a1",
            "c9e7535143804b5b87a03b84c4d4b60c",
            "3053e1ecc28a496cb00a4ac8a270b2b7",
            "3b7d9a13700f49769cbb98891790c10e",
            "bc896903d549471aa3d7be4832c4da55",
            "63c5297f08b24b6989c85d666c03b26d",
            "552afdec4c734f9e9dcf4d9f1aaa847c",
            "535225394d3f409baaac5b61e795cafc",
            "965ebb5ad01647268e26b9cdd1b0cb85",
            "2362caac5f924dd28ca27b55e92a6e15",
            "141ee07e7c584ae4b902d46adabee380",
            "31c717d7fd3f4847a1682fdc87093148",
            "0313b334e37b40959997003914e50e52",
            "cab5ff940716463697f7930d205a2780",
            "ff3b9749c1a0484cbc6ee093d1bdbcaf",
            "11166366f6054d38b0746c79cfbe7ae1",
            "691c9f2fc0054994bfa0db2034e37e0b",
            "07acf59918444ceb860d6b418f4b0b8d",
            "2cf832dfc8ab4d44a2e1ebf511600d7a",
            "7a89fe226437493bbd91979c828423a9",
            "c194014b76d34f02986324d09ab8b88f",
            "54ed6214221840e3ac5ae633e770d942",
            "adacc613fca7469380af0ab76c155392",
            "0cd644d3e6ad4ab28818605aea9f2479",
            "3a5c481d5682416cac431d33178a725c",
            "44565dbc47a042b88eaccf0abd6ed324",
            "ad6b50471c6d4376ae2cf895298bb863",
            "8fe62e44e34d456eb1d01c3f1a37bb16",
            "63717dd5030341faa2d194046846d2a8",
            "0570fcd177dd4d0c98e42bf932713692",
            "04aa0e86ff01458db8109ad1fe408a28",
            "15b7b421b25e476cb520459523d66c84",
            "ff2ddaff6b86412f9035f0cf4e98e08d",
            "272623367d144b85a4441044a6bc6f0b",
            "14a71102ac844582a34e1d4a6be2cfe7",
            "45d7a74d6176406f8ec1503d593a32fd",
            "aab7c53f02a947818769c41294ccdb39",
            "2e2190cd834b4739b7227b42c9bf054d",
            "0bbbbb3c94e5411caf0157bc6c88a8c1",
            "83e409b869d343c99604606388273588",
            "a4814c2926e04e35a004075b5af15ce0",
            "9811348e8d6848dfab24347ee3ff5fed",
            "7e97273431bd4cf0b441227218e516b1",
            "e9762fdf78dd4fb49677d5463496238d",
            "da3e7e96258d4ddb96bdee7710714d21",
            "a0a5ac8a57a0458e9820472224523716",
            "0eb8d121b2c44c01bc96de2511d467c7",
            "51912539a40b4ce18efcb5d1af4a6a42",
            "161e33e484044606bc41b61c4af581f2"
          ]
        },
        "id": "ly3D05en9ywI",
        "outputId": "c9215d20-eb40-42e0-db61-9f99de71fd2a"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Exception ignored in: <function PersistentDuckDB.__del__ at 0x7ec8f4690700>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/chromadb/db/duckdb.py\", line 446, in __del__\n",
            "    self.persist()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/chromadb/db/duckdb.py\", line 392, in persist\n",
            "    if not os.path.exists(self._save_folder):\n",
            "  File \"/usr/lib/python3.10/genericpath.py\", line 19, in exists\n",
            "    os.stat(path)\n",
            "TypeError: stat: path should be string, bytes, os.PathLike or integer, not NoneType\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5e43f1555c324656bd32a8db184e1bc1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "925273f24bdd4b7e8b529c662815cfac"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "63c5297f08b24b6989c85d666c03b26d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "691c9f2fc0054994bfa0db2034e37e0b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8fe62e44e34d456eb1d01c3f1a37bb16"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0bbbbb3c94e5411caf0157bc6c88a8c1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n",
            "pip install xformers.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "question = \"What's the latest news on space development?\"\n",
        "context = \" \".join([f\"#{str(i)}\" for i in results[\"documents\"][0]])\n",
        "prompt_template = f\"Relevant context: {context}\\n\\n The user's question: {question}\""
      ],
      "metadata": {
        "id": "2zkwyMOl972T"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lm_response = pipe(prompt_template)\n",
        "print(lm_response[0][\"generated_text\"])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Es1r64b-DcB",
        "outputId": "6f49e3a2-de0b-494a-a231-409384176d4f"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Relevant context: #Beck teams up with NASA and AI for 'Hyperspace' visual album experience #Orbital space tourism set for rebirth in 2021 #NASA drops \"insensitive\" nicknames for cosmic objects #‘It came alive:’ NASA astronauts describe experiencing splashdown in SpaceX Dragon #Hubble Uses Moon As “Mirror” to Study Earth’s Atmosphere – Proxy in Search of Potentially Habitable Planets Around Other Stars #Australia's small yet crucial part in the mission to find life on Mars #NASA Astronauts in SpaceX Capsule Splashdown in Gulf Of Mexico #SpaceX's Starship spacecraft saw 150 meters high #NASA’s InSight lander shows what’s beneath Mars’ surface #Alien base on Mercury: ET hunters claim to find huge UFO\n",
            "\n",
            " The user's question: What's the latest news on space development?\n",
            "\n",
            "I asked if it had been reported recently by my original question and received only vague, disorganized responses.\n",
            "\n",
            "\n",
            "I did get some great replies, ranging from \"Wow, this is awesome\" to \"My favourite science fiction episode of all time!\"\n",
            "\n",
            "\n",
            "I want to know if it will be an issue at Space Central, as there are some great places online to get involved.\n",
            "\n",
            "\n",
            "The answer: It will not.\n",
            "\n",
            "My first response is that if I think of the universe, I consider the universe all the more important.\n",
            "\n",
            "I also have many articles about space exploration in my books and I love their story as much as my books do.\n",
            "\n",
            "\n",
            "One of the most important things that I believe a space system should be building upon is the ability to study the atmospheres that the planets form on our galaxy.\n",
            "\n",
            "As discussed, the atmospheres on Mars and Earth are all very small bodies, and the planets produce their atmospheres a few orders of magnitude smaller than what would work in space on one of their satellites.\n",
            "\n",
            "Why? Because spaceflight is not about being a'spider' but about studying the planet itself and learning a little about its properties.\n",
            "\n",
            "Most people I know are very interested in studying the environment of the planet, the fact that there is absolutely nothing in our solar system (and other planets) that has an atmosphere and can move easily through the atmosphere; of course these things won't be readily available for anyone at home.\n",
            "\n",
            "All the above is so true, but so do the atmospheres on Mars, for when we get there, we'll need to study an environment as big as this, as much as possible.\n",
            "\n",
            "So what I'm looking for then will be the \"real\" atmosphere of the whole solar system.\n",
            "\n",
            "\n",
            "Because that is the only place that is completely flat, the planet itself should be a little different than the surface planets which are also in that situation, as well as potentially a bit more like the Earth.\n",
            "\n",
            "\n",
            "In fact, the atmosphere on Mars and Earth will probably be much colder than in orbit around Earth, given that the planet does not rotate much, and therefore the planet would be quite a bit colder on a par with the surface.\n",
            "\n",
            "All of this to say, that it's important to have a space system of your own, to explore the planet, to experiment with the atmospheres, to study its properties.\n",
            "\n",
            "But how to do that, if you're only interested in doing so from a\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IbLmE8FM-G8P"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}