RAG_LangChain_Deepseek.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "7cd27bf2605d4f42a578ff5cea830268": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_42866915ce1d4ecb92a92567365cdc11",
              "IPY_MODEL_6549f64b6568400d836d88075936e970",
              "IPY_MODEL_e60ed5097ec342db9bb32a1167b68647"
            ],
            "layout": "IPY_MODEL_43851139096f4d85b7f805279219312f"
          }
        },
        "42866915ce1d4ecb92a92567365cdc11": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_9121241c059f4804bce3446c08e37dae",
            "placeholder": "​",
            "style": "IPY_MODEL_2b27930b72014d00b1b4d3b337f1e561",
            "value": "config.json: 100%"
          }
        },
        "6549f64b6568400d836d88075936e970": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_aee1444e309741dfb9ae4811bea23c04",
            "max": 1272,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_abd2740362ba44a1b56933c8c975a0dd",
            "value": 1272
          }
        },
        "e60ed5097ec342db9bb32a1167b68647": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_713e0f83abc444e0b42bb23eac61d68f",
            "placeholder": "​",
            "style": "IPY_MODEL_27b857a8bacf425a876d5d1157208ee9",
            "value": " 1.27k/1.27k [00:00&lt;00:00, 86.8kB/s]"
          }
        },
        "43851139096f4d85b7f805279219312f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9121241c059f4804bce3446c08e37dae": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "2b27930b72014d00b1b4d3b337f1e561": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "aee1444e309741dfb9ae4811bea23c04": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "abd2740362ba44a1b56933c8c975a0dd": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "713e0f83abc444e0b42bb23eac61d68f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "27b857a8bacf425a876d5d1157208ee9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "e3f216ceb60a450fa771a1c01b8195b7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_10e13b909a3646808d6fdfa267cd963a",
              "IPY_MODEL_2a1dfc3b870f4af78316844454e0135b",
              "IPY_MODEL_5191a002d4e14bfcb6add27bccef5561"
            ],
            "layout": "IPY_MODEL_43ce9fa01b644e9fa644895945694c92"
          }
        },
        "10e13b909a3646808d6fdfa267cd963a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_f94cdc83e7444aed8885382b209d7ee8",
            "placeholder": "​",
            "style": "IPY_MODEL_03f0c117b481489881450c1208c0dcba",
            "value": "model.safetensors: 100%"
          }
        },
        "2a1dfc3b870f4af78316844454e0135b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_0213d1a4472b41c8ac046fb3b3bb19e9",
            "max": 3087130976,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_1304b646f28a46f4853941015c7c3ae1",
            "value": 3087130976
          }
        },
        "5191a002d4e14bfcb6add27bccef5561": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_4a1a3d54644343feb8105afed3779f00",
            "placeholder": "​",
            "style": "IPY_MODEL_05eca1c841e3439a929d258509236528",
            "value": " 3.09G/3.09G [01:12&lt;00:00, 42.1MB/s]"
          }
        },
        "43ce9fa01b644e9fa644895945694c92": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "f94cdc83e7444aed8885382b209d7ee8": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "03f0c117b481489881450c1208c0dcba": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "0213d1a4472b41c8ac046fb3b3bb19e9": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "1304b646f28a46f4853941015c7c3ae1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "4a1a3d54644343feb8105afed3779f00": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "05eca1c841e3439a929d258509236528": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "60c615afb4ea46c98bc3e777282c85de": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_2a49d49a36bc43afa89380ea6c4c698b",
              "IPY_MODEL_800e623399ac4337978123a212bcb04a",
              "IPY_MODEL_8869bb534f72474782851da11a8bb712"
            ],
            "layout": "IPY_MODEL_13d6d8d9214c4da093d50a95438be301"
          }
        },
        "2a49d49a36bc43afa89380ea6c4c698b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_1959466725a146aa889ac0a2dd1a561b",
            "placeholder": "​",
            "style": "IPY_MODEL_cc141014cf524d208645eb86c1b43333",
            "value": "generation_config.json: 100%"
          }
        },
        "800e623399ac4337978123a212bcb04a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_4adae18837714f1b87ddb90cd80206e8",
            "max": 3903,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_274a673ba8804ec4afb641161c6d34bf",
            "value": 3903
          }
        },
        "8869bb534f72474782851da11a8bb712": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_d4696ec30cb84aa5b52d676192192c86",
            "placeholder": "​",
            "style": "IPY_MODEL_880ffdc7c9ab4c408d8ed782b4b202a4",
            "value": " 3.90k/3.90k [00:00&lt;00:00, 208kB/s]"
          }
        },
        "13d6d8d9214c4da093d50a95438be301": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "1959466725a146aa889ac0a2dd1a561b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "cc141014cf524d208645eb86c1b43333": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "4adae18837714f1b87ddb90cd80206e8": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "274a673ba8804ec4afb641161c6d34bf": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "d4696ec30cb84aa5b52d676192192c86": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "880ffdc7c9ab4c408d8ed782b4b202a4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "d58d5747eda543799ea2ee843810adbf": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_fab44c82bd474f2f9f5b68cb3ea1e4c8",
              "IPY_MODEL_f71a60bcf5834c3bbd679349b7c08cf2",
              "IPY_MODEL_f101840a3b0b4717a11e9050330bcd9d"
            ],
            "layout": "IPY_MODEL_29c541f3d2844fb1ba3b1b47444010cf"
          }
        },
        "fab44c82bd474f2f9f5b68cb3ea1e4c8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_1e5e20492c324e0081e36b7db140894b",
            "placeholder": "​",
            "style": "IPY_MODEL_98cba063fa9844e299e4139568beccdf",
            "value": "tokenizer_config.json: 100%"
          }
        },
        "f71a60bcf5834c3bbd679349b7c08cf2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_a70fea994b68491dbba514afb56dd137",
            "max": 282843,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_188e9962e93a43749b65dc58042dfe90",
            "value": 282843
          }
        },
        "f101840a3b0b4717a11e9050330bcd9d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_ab6e19eafaa24094ac2dc2059aa4970a",
            "placeholder": "​",
            "style": "IPY_MODEL_a15997e2b5114e818ec5add42e0298a8",
            "value": " 283k/283k [00:00&lt;00:00, 5.47MB/s]"
          }
        },
        "29c541f3d2844fb1ba3b1b47444010cf": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "1e5e20492c324e0081e36b7db140894b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "98cba063fa9844e299e4139568beccdf": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "a70fea994b68491dbba514afb56dd137": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "188e9962e93a43749b65dc58042dfe90": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "ab6e19eafaa24094ac2dc2059aa4970a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a15997e2b5114e818ec5add42e0298a8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "d58171a5916240eeb2869cd662207efa": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_f75954f133234ffbbc19b921f9fd1eeb",
              "IPY_MODEL_7e8d8fcf9a514048bb4f5678957eca01",
              "IPY_MODEL_f08b456336d0487a865d3880a2b1242f"
            ],
            "layout": "IPY_MODEL_96d783870fa44526b7de0009babc22eb"
          }
        },
        "f75954f133234ffbbc19b921f9fd1eeb": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_bd359b1a906b483191dd04459c82d90d",
            "placeholder": "​",
            "style": "IPY_MODEL_b929e29b54cc4738a033950ef7419747",
            "value": "vocab.json: 100%"
          }
        },
        "7e8d8fcf9a514048bb4f5678957eca01": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_8e72ce788f524c208a95057f55f4bcbc",
            "max": 1036558,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_ca619e3779284cab8dd706e8ac525c7a",
            "value": 1036558
          }
        },
        "f08b456336d0487a865d3880a2b1242f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_88a2cd22959147239e0c8453d21eb407",
            "placeholder": "​",
            "style": "IPY_MODEL_e162653cbd4140719f8f0d3b974c182f",
            "value": " 1.04M/1.04M [00:00&lt;00:00, 32.6MB/s]"
          }
        },
        "96d783870fa44526b7de0009babc22eb": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "bd359b1a906b483191dd04459c82d90d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "b929e29b54cc4738a033950ef7419747": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "8e72ce788f524c208a95057f55f4bcbc": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "ca619e3779284cab8dd706e8ac525c7a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "88a2cd22959147239e0c8453d21eb407": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e162653cbd4140719f8f0d3b974c182f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "fa4d050acb164f4aac7306251ec18302": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_13841b77c0d248ce947479f2c8f9ed10",
              "IPY_MODEL_7611800d2f9d426da92fead76ed47b05",
              "IPY_MODEL_eabc39a733b34174a2b2c4fb267d6edd"
            ],
            "layout": "IPY_MODEL_261e80d067a24b489c2a88ee1f8d5616"
          }
        },
        "13841b77c0d248ce947479f2c8f9ed10": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_32a1ede9675c477087ad7dcea2ef76cf",
            "placeholder": "​",
            "style": "IPY_MODEL_e54a89d7d22a413a985a806c1c0dc6f2",
            "value": "tokenizer.json: 100%"
          }
        },
        "7611800d2f9d426da92fead76ed47b05": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_5418a71eafe3460c8ecda807dc0c9c81",
            "max": 2480617,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_3462975448cc47448f039e41dbcb5ac2",
            "value": 2480617
          }
        },
        "eabc39a733b34174a2b2c4fb267d6edd": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_8a24ee76c6b7454a9e830a57c4d67565",
            "placeholder": "​",
            "style": "IPY_MODEL_0dcd3125a61545a4a8a66fc5d3b7bff1",
            "value": " 2.48M/2.48M [00:00&lt;00:00, 13.2MB/s]"
          }
        },
        "261e80d067a24b489c2a88ee1f8d5616": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "32a1ede9675c477087ad7dcea2ef76cf": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e54a89d7d22a413a985a806c1c0dc6f2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "5418a71eafe3460c8ecda807dc0c9c81": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "3462975448cc47448f039e41dbcb5ac2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "8a24ee76c6b7454a9e830a57c4d67565": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "0dcd3125a61545a4a8a66fc5d3b7bff1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "7efcc6e1b77b4265a35615c146010a86": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_ebd973d8b25c4623bfa9c6e1cb9b9781",
              "IPY_MODEL_6271d077a7f74d049c89724462d0b5b5",
              "IPY_MODEL_cc6b89b4d64a4067a138bfa1d8f6ebd4"
            ],
            "layout": "IPY_MODEL_003921a693be4a37885f67641532886c"
          }
        },
        "ebd973d8b25c4623bfa9c6e1cb9b9781": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_cf355d877aa6454b8e30f576995c0e28",
            "placeholder": "​",
            "style": "IPY_MODEL_5152cc38021a488ea935f2bfe6bb23fb",
            "value": "merges.txt: 100%"
          }
        },
        "6271d077a7f74d049c89724462d0b5b5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_9317c8d548f846e4b46a9bb295ff5bae",
            "max": 493869,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_fa22a0cf533e4f79a03c5caf38a5bc37",
            "value": 493869
          }
        },
        "cc6b89b4d64a4067a138bfa1d8f6ebd4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_12b347d6ba894f18a817ea698e376cc4",
            "placeholder": "​",
            "style": "IPY_MODEL_338893f3233247789fe3a03e1d7fdb51",
            "value": " 494k/494k [00:00&lt;00:00, 26.6MB/s]"
          }
        },
        "003921a693be4a37885f67641532886c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "cf355d877aa6454b8e30f576995c0e28": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "5152cc38021a488ea935f2bfe6bb23fb": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "9317c8d548f846e4b46a9bb295ff5bae": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "fa22a0cf533e4f79a03c5caf38a5bc37": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "12b347d6ba894f18a817ea698e376cc4": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "338893f3233247789fe3a03e1d7fdb51": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "35ffefe926364c69a6517be47cf88898": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_873dafff73ff4236aea46488eed1c1d7",
              "IPY_MODEL_86b3be79c7c44cdab1dc56a1dfff9bdb",
              "IPY_MODEL_f2e36274161a473b83b448fd83bee495"
            ],
            "layout": "IPY_MODEL_6cf5ebb8300146fd8b12b0f426796503"
          }
        },
        "873dafff73ff4236aea46488eed1c1d7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_0949279f3d7e4b9d83fcf9e63384f063",
            "placeholder": "​",
            "style": "IPY_MODEL_9118aa8b14654bf6811971f377d0110b",
            "value": "normalizer.json: 100%"
          }
        },
        "86b3be79c7c44cdab1dc56a1dfff9bdb": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_ae9b5e020fb64d1dab1dca8d7580dbef",
            "max": 52666,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_2fc411a517394b34af8ffb9b47f23f83",
            "value": 52666
          }
        },
        "f2e36274161a473b83b448fd83bee495": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_34a10dd1ab6845419d2d87b93dbebaf2",
            "placeholder": "​",
            "style": "IPY_MODEL_f0cb4a21e73744488493fa81625c7f8c",
            "value": " 52.7k/52.7k [00:00&lt;00:00, 2.90MB/s]"
          }
        },
        "6cf5ebb8300146fd8b12b0f426796503": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "0949279f3d7e4b9d83fcf9e63384f063": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9118aa8b14654bf6811971f377d0110b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "ae9b5e020fb64d1dab1dca8d7580dbef": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "2fc411a517394b34af8ffb9b47f23f83": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "34a10dd1ab6845419d2d87b93dbebaf2": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "f0cb4a21e73744488493fa81625c7f8c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "532e6b5d6387467fb4e8db0f1ce74407": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_dcad3ebcf1fc4aebb14e1a966463fe9d",
              "IPY_MODEL_ce47b5579e7a4df7b02c92158ec68c35",
              "IPY_MODEL_b50fa1a08c4e48cfaa1c93bee498812d"
            ],
            "layout": "IPY_MODEL_e6014d4864164bca84c39162f8fec64f"
          }
        },
        "dcad3ebcf1fc4aebb14e1a966463fe9d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_bdc553ab99ac41f2865a4aa2bb81b57c",
            "placeholder": "​",
            "style": "IPY_MODEL_a23cbac25a1b432ea7443e09f2a5e839",
            "value": "added_tokens.json: 100%"
          }
        },
        "ce47b5579e7a4df7b02c92158ec68c35": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_cc30d9b6e91541678afcfa5c1bdb8401",
            "max": 34648,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_431661cb99da4cbe9b5d86517f3b85f5",
            "value": 34648
          }
        },
        "b50fa1a08c4e48cfaa1c93bee498812d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_584f98c515604e5f831c18dc40106807",
            "placeholder": "​",
            "style": "IPY_MODEL_1bc68202fe81403283658a382f9e7229",
            "value": " 34.6k/34.6k [00:00&lt;00:00, 2.76MB/s]"
          }
        },
        "e6014d4864164bca84c39162f8fec64f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "bdc553ab99ac41f2865a4aa2bb81b57c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a23cbac25a1b432ea7443e09f2a5e839": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "cc30d9b6e91541678afcfa5c1bdb8401": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "431661cb99da4cbe9b5d86517f3b85f5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "584f98c515604e5f831c18dc40106807": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "1bc68202fe81403283658a382f9e7229": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "3a1c454f2a58409996901acb1291ffd6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_f932c4a5b78b441fae6c2df051cfd34b",
              "IPY_MODEL_18f696bb727049d3937e2fa70f453ae2",
              "IPY_MODEL_2ed9357ef5464c3496d29bc388283fa3"
            ],
            "layout": "IPY_MODEL_bfa87a4891e94d31b5379fc601df83dc"
          }
        },
        "f932c4a5b78b441fae6c2df051cfd34b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_ef1e00e7f8184df1b97ca95de9adfc5f",
            "placeholder": "​",
            "style": "IPY_MODEL_b3d6976a6dc8400cb5b93d828f12bcc1",
            "value": "special_tokens_map.json: 100%"
          }
        },
        "18f696bb727049d3937e2fa70f453ae2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_006ea14b941c4107bd37ef1f3bf4fe50",
            "max": 2072,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_5ed9486fa4f9406990f9021aae794dcc",
            "value": 2072
          }
        },
        "2ed9357ef5464c3496d29bc388283fa3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_3a86142e8eb6413699670116513b49af",
            "placeholder": "​",
            "style": "IPY_MODEL_8b9206dcb6264dff846448874dee48e8",
            "value": " 2.07k/2.07k [00:00&lt;00:00, 129kB/s]"
          }
        },
        "bfa87a4891e94d31b5379fc601df83dc": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "ef1e00e7f8184df1b97ca95de9adfc5f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "b3d6976a6dc8400cb5b93d828f12bcc1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "006ea14b941c4107bd37ef1f3bf4fe50": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "5ed9486fa4f9406990f9021aae794dcc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "3a86142e8eb6413699670116513b49af": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "8b9206dcb6264dff846448874dee48e8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "48768421138f454d9f01a8fd354830d0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_7b879d2278f24d11809cf0e5218368f7",
              "IPY_MODEL_e8284eb2b93c43b2abce65c1526e1056",
              "IPY_MODEL_9ae2788af6cf4efd8e367da6b8d2833b"
            ],
            "layout": "IPY_MODEL_252d4ec8aade409da054544dfb352341"
          }
        },
        "7b879d2278f24d11809cf0e5218368f7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_89c7010a422642c2bf963a131abd9c0e",
            "placeholder": "​",
            "style": "IPY_MODEL_6cc96f4f01f84c2aa1f5fcf7191f7c17",
            "value": "preprocessor_config.json: 100%"
          }
        },
        "e8284eb2b93c43b2abce65c1526e1056": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_9a463457254043b99b9f2df1cdf90c7d",
            "max": 340,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_1723a47885ad433c85f24d54fe07ef2a",
            "value": 340
          }
        },
        "9ae2788af6cf4efd8e367da6b8d2833b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_f3756bbd97804f7d86f6187f935b91a1",
            "placeholder": "​",
            "style": "IPY_MODEL_8fccc011526f4fc2be92657b6fdea816",
            "value": " 340/340 [00:00&lt;00:00, 21.7kB/s]"
          }
        },
        "252d4ec8aade409da054544dfb352341": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "89c7010a422642c2bf963a131abd9c0e": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "6cc96f4f01f84c2aa1f5fcf7191f7c17": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "9a463457254043b99b9f2df1cdf90c7d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "1723a47885ad433c85f24d54fe07ef2a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "f3756bbd97804f7d86f6187f935b91a1": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "8fccc011526f4fc2be92657b6fdea816": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "8d141e3087374674a4e559f603f2c3d0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_bce4a0939f054479a0a1e36e9b95d0b4",
              "IPY_MODEL_a8d18f63e04c49b6b104634af0c0d384",
              "IPY_MODEL_35cd2a087d674d629585ba141b10a010"
            ],
            "layout": "IPY_MODEL_608dfdd5c5ea4344a3868bf4436c4cd3"
          }
        },
        "bce4a0939f054479a0a1e36e9b95d0b4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_5f9fe824589e41d59dc90de047a46d89",
            "placeholder": "​",
            "style": "IPY_MODEL_1d8fed6207ea43edbe5203828176fd06",
            "value": "tokenizer_config.json: 100%"
          }
        },
        "a8d18f63e04c49b6b104634af0c0d384": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_5db980f9d9ef4f42a0e73bb64e4ac8fb",
            "max": 3061,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_e271b90654c543e593a38d0e7786785c",
            "value": 3061
          }
        },
        "35cd2a087d674d629585ba141b10a010": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_b264960dff344f679ade6102ab3f445b",
            "placeholder": "​",
            "style": "IPY_MODEL_37fb3be92db441cbb17190630c4a7688",
            "value": " 3.06k/3.06k [00:00&lt;00:00, 245kB/s]"
          }
        },
        "608dfdd5c5ea4344a3868bf4436c4cd3": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "5f9fe824589e41d59dc90de047a46d89": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "1d8fed6207ea43edbe5203828176fd06": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "5db980f9d9ef4f42a0e73bb64e4ac8fb": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e271b90654c543e593a38d0e7786785c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "b264960dff344f679ade6102ab3f445b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "37fb3be92db441cbb17190630c4a7688": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "198e630f28d14ab4ba9019b6c797fff3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_1e705f3260774c36ba42035a9ab813d5",
              "IPY_MODEL_d0e048a28c8f4188ae1f38936b00df5f",
              "IPY_MODEL_42de39bb68894e52a62a80ac40860d54"
            ],
            "layout": "IPY_MODEL_c475243aabbc4b7891b84ceddd26e7fa"
          }
        },
        "1e705f3260774c36ba42035a9ab813d5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_d27c5a45fcd5479c8d5f294e60b47530",
            "placeholder": "​",
            "style": "IPY_MODEL_4e6d1a54485d4202a08cc827482db0a6",
            "value": "tokenizer.json: 100%"
          }
        },
        "d0e048a28c8f4188ae1f38936b00df5f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_bad1835aef574c619d61f89428c66d5c",
            "max": 7031660,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_341e89284a3341a4b1d8582d857fca6b",
            "value": 7031660
          }
        },
        "42de39bb68894e52a62a80ac40860d54": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_89385523c57e4cfda56242e1972f466e",
            "placeholder": "​",
            "style": "IPY_MODEL_3215ab08151e46ccaa2d73d3dedb8ce9",
            "value": " 7.03M/7.03M [00:00&lt;00:00, 23.6MB/s]"
          }
        },
        "c475243aabbc4b7891b84ceddd26e7fa": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d27c5a45fcd5479c8d5f294e60b47530": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "4e6d1a54485d4202a08cc827482db0a6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "bad1835aef574c619d61f89428c66d5c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "341e89284a3341a4b1d8582d857fca6b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "89385523c57e4cfda56242e1972f466e": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "3215ab08151e46ccaa2d73d3dedb8ce9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "48f337701d5742559c43a831d60e012b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_e170f64209eb4f13862c3be912626057",
              "IPY_MODEL_b86447609b624b5ab8b9d0f52017bcee",
              "IPY_MODEL_f674d5ecbe90410d955989c7fa68c41b"
            ],
            "layout": "IPY_MODEL_451a00d6e50e46d1b6cec1a7e8d69821"
          }
        },
        "e170f64209eb4f13862c3be912626057": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_4a55220ab45f4230823d1184fe9ca3c4",
            "placeholder": "​",
            "style": "IPY_MODEL_ef3080f94eb2489ba2e2e49a5290aa61",
            "value": "config.json: 100%"
          }
        },
        "b86447609b624b5ab8b9d0f52017bcee": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_7be879343c7d48eebb21636604b9fff6",
            "max": 679,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_48c561f8ec2b4430a253726cd579e1f8",
            "value": 679
          }
        },
        "f674d5ecbe90410d955989c7fa68c41b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_49d18b4a704c487592cb70c5b7b06170",
            "placeholder": "​",
            "style": "IPY_MODEL_d58fbd4dd2e4413da099cc1682308715",
            "value": " 679/679 [00:00&lt;00:00, 29.8kB/s]"
          }
        },
        "451a00d6e50e46d1b6cec1a7e8d69821": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "4a55220ab45f4230823d1184fe9ca3c4": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "ef3080f94eb2489ba2e2e49a5290aa61": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "7be879343c7d48eebb21636604b9fff6": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "48c561f8ec2b4430a253726cd579e1f8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "49d18b4a704c487592cb70c5b7b06170": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d58fbd4dd2e4413da099cc1682308715": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "d344c3d5b31f4935b873b0ab2bc08c4a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_4d7da6dae87947deaa4871efd9ad5fd7",
              "IPY_MODEL_12a304d93ab44e738b3375253c5f1752",
              "IPY_MODEL_56ede499bf9348cbac2407b952121645"
            ],
            "layout": "IPY_MODEL_ee76eaa4672e4ec2862ef06ba5e88f94"
          }
        },
        "4d7da6dae87947deaa4871efd9ad5fd7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_922128d9478842ad811b47c4268a3e1f",
            "placeholder": "​",
            "style": "IPY_MODEL_4279749883144419a2baef5a1b81c6d6",
            "value": "model.safetensors: 100%"
          }
        },
        "12a304d93ab44e738b3375253c5f1752": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_076f99e7deb04579a40e9f1694cc43ff",
            "max": 3554214621,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_662c4c77fd1849e49ba7b13bd958c295",
            "value": 3554214621
          }
        },
        "56ede499bf9348cbac2407b952121645": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_69d5f68e66e84cbe8a4c92abd4986312",
            "placeholder": "​",
            "style": "IPY_MODEL_b3dda04fb18c45d8a6b9b852a5ee6c63",
            "value": " 3.55G/3.55G [01:24&lt;00:00, 42.0MB/s]"
          }
        },
        "ee76eaa4672e4ec2862ef06ba5e88f94": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "922128d9478842ad811b47c4268a3e1f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "4279749883144419a2baef5a1b81c6d6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "076f99e7deb04579a40e9f1694cc43ff": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "662c4c77fd1849e49ba7b13bd958c295": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "69d5f68e66e84cbe8a4c92abd4986312": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "b3dda04fb18c45d8a6b9b852a5ee6c63": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "8ea41800c6154c8a8fca82a119147334": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_56bd66b9279d4090b22559a326548b8f",
              "IPY_MODEL_5f4916aeb2e441b0960b06fefb75fb2d",
              "IPY_MODEL_a33da8d325cb4433af853bd16c5454d2"
            ],
            "layout": "IPY_MODEL_c32dd20ae9bd4c6c84d24d4f065cf319"
          }
        },
        "56bd66b9279d4090b22559a326548b8f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_eb66fcd18ed24b66bbba39e9f06b5a72",
            "placeholder": "​",
            "style": "IPY_MODEL_d3b10247cd2d4320bd71db599ca47691",
            "value": "generation_config.json: 100%"
          }
        },
        "5f4916aeb2e441b0960b06fefb75fb2d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_70ce7673ef204786a656b38958209e8d",
            "max": 181,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_84d9caf516c448ee8566c935f23c8b60",
            "value": 181
          }
        },
        "a33da8d325cb4433af853bd16c5454d2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_0a415db460424707800a0a2fa5677a55",
            "placeholder": "​",
            "style": "IPY_MODEL_bc8b11832f004c829fd6f6850718cd1c",
            "value": " 181/181 [00:00&lt;00:00, 7.94kB/s]"
          }
        },
        "c32dd20ae9bd4c6c84d24d4f065cf319": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "eb66fcd18ed24b66bbba39e9f06b5a72": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d3b10247cd2d4320bd71db599ca47691": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "70ce7673ef204786a656b38958209e8d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "84d9caf516c448ee8566c935f23c8b60": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "0a415db460424707800a0a2fa5677a55": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "bc8b11832f004c829fd6f6850718cd1c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "Auac9BQuSG6V",
        "outputId": "d44398fe-9706-41de-b9e7-489bb002fc87"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/93.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m93.3/93.3 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m69.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m412.7/412.7 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting langchain_community\n",
            "  Downloading langchain_community-0.3.16-py3-none-any.whl.metadata (2.9 kB)\n",
            "Requirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.11/dist-packages (from langchain_community) (6.0.2)\n",
            "Requirement already satisfied: SQLAlchemy<3,>=1.4 in /usr/local/lib/python3.11/dist-packages (from langchain_community) (2.0.37)\n",
            "Requirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /usr/local/lib/python3.11/dist-packages (from langchain_community) (3.11.11)\n",
            "Collecting dataclasses-json<0.7,>=0.5.7 (from langchain_community)\n",
            "  Downloading dataclasses_json-0.6.7-py3-none-any.whl.metadata (25 kB)\n",
            "Requirement already satisfied: httpx-sse<0.5.0,>=0.4.0 in /usr/local/lib/python3.11/dist-packages (from langchain_community) (0.4.0)\n",
            "Requirement already satisfied: langchain<0.4.0,>=0.3.16 in /usr/local/lib/python3.11/dist-packages (from langchain_community) (0.3.16)\n",
            "Requirement already satisfied: langchain-core<0.4.0,>=0.3.32 in /usr/local/lib/python3.11/dist-packages (from langchain_community) (0.3.33)\n",
            "Requirement already satisfied: langsmith<0.4,>=0.1.125 in /usr/local/lib/python3.11/dist-packages (from langchain_community) (0.3.2)\n",
            "Requirement already satisfied: numpy<2,>=1.22.4 in /usr/local/lib/python3.11/dist-packages (from langchain_community) (1.26.4)\n",
            "Collecting pydantic-settings<3.0.0,>=2.4.0 (from langchain_community)\n",
            "  Downloading pydantic_settings-2.7.1-py3-none-any.whl.metadata (3.5 kB)\n",
            "Requirement already satisfied: requests<3,>=2 in /usr/local/lib/python3.11/dist-packages (from langchain_community) (2.32.3)\n",
            "Requirement already satisfied: tenacity!=8.4.0,<10,>=8.1.0 in /usr/local/lib/python3.11/dist-packages (from langchain_community) (9.0.0)\n",
            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (2.4.4)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.11/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (1.3.2)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (25.1.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.11/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (1.5.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.11/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (6.1.0)\n",
            "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (0.2.1)\n",
            "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (1.18.3)\n",
            "Collecting marshmallow<4.0.0,>=3.18.0 (from dataclasses-json<0.7,>=0.5.7->langchain_community)\n",
            "  Downloading marshmallow-3.26.1-py3-none-any.whl.metadata (7.3 kB)\n",
            "Collecting typing-inspect<1,>=0.4.0 (from dataclasses-json<0.7,>=0.5.7->langchain_community)\n",
            "  Downloading typing_inspect-0.9.0-py3-none-any.whl.metadata (1.5 kB)\n",
            "Requirement already satisfied: langchain-text-splitters<0.4.0,>=0.3.3 in /usr/local/lib/python3.11/dist-packages (from langchain<0.4.0,>=0.3.16->langchain_community) (0.3.5)\n",
            "Requirement already satisfied: pydantic<3.0.0,>=2.7.4 in /usr/local/lib/python3.11/dist-packages (from langchain<0.4.0,>=0.3.16->langchain_community) (2.10.6)\n",
            "Requirement already satisfied: jsonpatch<2.0,>=1.33 in /usr/local/lib/python3.11/dist-packages (from langchain-core<0.4.0,>=0.3.32->langchain_community) (1.33)\n",
            "Requirement already satisfied: packaging<25,>=23.2 in /usr/local/lib/python3.11/dist-packages (from langchain-core<0.4.0,>=0.3.32->langchain_community) (24.2)\n",
            "Requirement already satisfied: typing-extensions>=4.7 in /usr/local/lib/python3.11/dist-packages (from langchain-core<0.4.0,>=0.3.32->langchain_community) (4.12.2)\n",
            "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from langsmith<0.4,>=0.1.125->langchain_community) (0.28.1)\n",
            "Requirement already satisfied: orjson<4.0.0,>=3.9.14 in /usr/local/lib/python3.11/dist-packages (from langsmith<0.4,>=0.1.125->langchain_community) (3.10.15)\n",
            "Requirement already satisfied: requests-toolbelt<2.0.0,>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from langsmith<0.4,>=0.1.125->langchain_community) (1.0.0)\n",
            "Requirement already satisfied: zstandard<0.24.0,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from langsmith<0.4,>=0.1.125->langchain_community) (0.23.0)\n",
            "Collecting python-dotenv>=0.21.0 (from pydantic-settings<3.0.0,>=2.4.0->langchain_community)\n",
            "  Downloading python_dotenv-1.0.1-py3-none-any.whl.metadata (23 kB)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2->langchain_community) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2->langchain_community) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2->langchain_community) (2.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2->langchain_community) (2024.12.14)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.11/dist-packages (from SQLAlchemy<3,>=1.4->langchain_community) (3.1.1)\n",
            "Requirement already satisfied: anyio in /usr/local/lib/python3.11/dist-packages (from httpx<1,>=0.23.0->langsmith<0.4,>=0.1.125->langchain_community) (3.7.1)\n",
            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.11/dist-packages (from httpx<1,>=0.23.0->langsmith<0.4,>=0.1.125->langchain_community) (1.0.7)\n",
            "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.11/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->langsmith<0.4,>=0.1.125->langchain_community) (0.14.0)\n",
            "Requirement already satisfied: jsonpointer>=1.9 in /usr/local/lib/python3.11/dist-packages (from jsonpatch<2.0,>=1.33->langchain-core<0.4.0,>=0.3.32->langchain_community) (3.0.0)\n",
            "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.11/dist-packages (from pydantic<3.0.0,>=2.7.4->langchain<0.4.0,>=0.3.16->langchain_community) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.27.2 in /usr/local/lib/python3.11/dist-packages (from pydantic<3.0.0,>=2.7.4->langchain<0.4.0,>=0.3.16->langchain_community) (2.27.2)\n",
            "Collecting mypy-extensions>=0.3.0 (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain_community)\n",
            "  Downloading mypy_extensions-1.0.0-py3-none-any.whl.metadata (1.1 kB)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.11/dist-packages (from anyio->httpx<1,>=0.23.0->langsmith<0.4,>=0.1.125->langchain_community) (1.3.1)\n",
            "Downloading langchain_community-0.3.16-py3-none-any.whl (2.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m33.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading dataclasses_json-0.6.7-py3-none-any.whl (28 kB)\n",
            "Downloading pydantic_settings-2.7.1-py3-none-any.whl (29 kB)\n",
            "Downloading marshmallow-3.26.1-py3-none-any.whl (50 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.9/50.9 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading python_dotenv-1.0.1-py3-none-any.whl (19 kB)\n",
            "Downloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB)\n",
            "Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)\n",
            "Installing collected packages: python-dotenv, mypy-extensions, marshmallow, typing-inspect, pydantic-settings, dataclasses-json, langchain_community\n",
            "Successfully installed dataclasses-json-0.6.7 langchain_community-0.3.16 marshmallow-3.26.1 mypy-extensions-1.0.0 pydantic-settings-2.7.1 python-dotenv-1.0.1 typing-inspect-0.9.0\n",
            "Collecting langchain-huggingface\n",
            "  Downloading langchain_huggingface-0.1.2-py3-none-any.whl.metadata (1.3 kB)\n",
            "Requirement already satisfied: huggingface-hub>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from langchain-huggingface) (0.27.1)\n",
            "Requirement already satisfied: langchain-core<0.4.0,>=0.3.15 in /usr/local/lib/python3.11/dist-packages (from langchain-huggingface) (0.3.33)\n",
            "Requirement already satisfied: sentence-transformers>=2.6.0 in /usr/local/lib/python3.11/dist-packages (from langchain-huggingface) (3.3.1)\n",
            "Requirement already satisfied: tokenizers>=0.19.1 in /usr/local/lib/python3.11/dist-packages (from langchain-huggingface) (0.21.0)\n",
            "Requirement already satisfied: transformers>=4.39.0 in /usr/local/lib/python3.11/dist-packages (from langchain-huggingface) (4.47.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.23.0->langchain-huggingface) (3.17.0)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.23.0->langchain-huggingface) (2024.10.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.23.0->langchain-huggingface) (24.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.23.0->langchain-huggingface) (6.0.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.23.0->langchain-huggingface) (2.32.3)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.23.0->langchain-huggingface) (4.67.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.23.0->langchain-huggingface) (4.12.2)\n",
            "Requirement already satisfied: jsonpatch<2.0,>=1.33 in /usr/local/lib/python3.11/dist-packages (from langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (1.33)\n",
            "Requirement already satisfied: langsmith<0.4,>=0.1.125 in /usr/local/lib/python3.11/dist-packages (from langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (0.3.2)\n",
            "Requirement already satisfied: pydantic<3.0.0,>=2.5.2 in /usr/local/lib/python3.11/dist-packages (from langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (2.10.6)\n",
            "Requirement already satisfied: tenacity!=8.4.0,<10.0.0,>=8.1.0 in /usr/local/lib/python3.11/dist-packages (from langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (9.0.0)\n",
            "Requirement already satisfied: torch>=1.11.0 in /usr/local/lib/python3.11/dist-packages (from sentence-transformers>=2.6.0->langchain-huggingface) (2.5.1+cu124)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (from sentence-transformers>=2.6.0->langchain-huggingface) (1.6.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from sentence-transformers>=2.6.0->langchain-huggingface) (1.13.1)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.11/dist-packages (from sentence-transformers>=2.6.0->langchain-huggingface) (11.1.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from transformers>=4.39.0->langchain-huggingface) (1.26.4)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.11/dist-packages (from transformers>=4.39.0->langchain-huggingface) (2024.11.6)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.11/dist-packages (from transformers>=4.39.0->langchain-huggingface) (0.5.2)\n",
            "Requirement already satisfied: jsonpointer>=1.9 in /usr/local/lib/python3.11/dist-packages (from jsonpatch<2.0,>=1.33->langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (3.0.0)\n",
            "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from langsmith<0.4,>=0.1.125->langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (0.28.1)\n",
            "Requirement already satisfied: orjson<4.0.0,>=3.9.14 in /usr/local/lib/python3.11/dist-packages (from langsmith<0.4,>=0.1.125->langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (3.10.15)\n",
            "Requirement already satisfied: requests-toolbelt<2.0.0,>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from langsmith<0.4,>=0.1.125->langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (1.0.0)\n",
            "Requirement already satisfied: zstandard<0.24.0,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from langsmith<0.4,>=0.1.125->langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (0.23.0)\n",
            "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.11/dist-packages (from pydantic<3.0.0,>=2.5.2->langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.27.2 in /usr/local/lib/python3.11/dist-packages (from pydantic<3.0.0,>=2.5.2->langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (2.27.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.23.0->langchain-huggingface) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.23.0->langchain-huggingface) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.23.0->langchain-huggingface) (2.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.23.0->langchain-huggingface) (2024.12.14)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain-huggingface) (3.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain-huggingface) (3.1.5)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain-huggingface)\n",
            "  Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.4.127 (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain-huggingface)\n",
            "  Downloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.4.127 (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain-huggingface)\n",
            "  Downloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain-huggingface)\n",
            "  Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cublas-cu12==12.4.5.8 (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain-huggingface)\n",
            "  Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cufft-cu12==11.2.1.3 (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain-huggingface)\n",
            "  Downloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-curand-cu12==10.3.5.147 (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain-huggingface)\n",
            "  Downloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cusolver-cu12==11.6.1.9 (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain-huggingface)\n",
            "  Downloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cusparse-cu12==12.3.1.170 (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain-huggingface)\n",
            "  Downloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain-huggingface) (2.21.5)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain-huggingface) (12.4.127)\n",
            "Collecting nvidia-nvjitlink-cu12==12.4.127 (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain-huggingface)\n",
            "  Downloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Requirement already satisfied: triton==3.1.0 in /usr/local/lib/python3.11/dist-packages (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain-huggingface) (3.1.0)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain-huggingface) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch>=1.11.0->sentence-transformers>=2.6.0->langchain-huggingface) (1.3.0)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn->sentence-transformers>=2.6.0->langchain-huggingface) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn->sentence-transformers>=2.6.0->langchain-huggingface) (3.5.0)\n",
            "Requirement already satisfied: anyio in /usr/local/lib/python3.11/dist-packages (from httpx<1,>=0.23.0->langsmith<0.4,>=0.1.125->langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (3.7.1)\n",
            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.11/dist-packages (from httpx<1,>=0.23.0->langsmith<0.4,>=0.1.125->langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (1.0.7)\n",
            "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.11/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->langsmith<0.4,>=0.1.125->langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (0.14.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch>=1.11.0->sentence-transformers>=2.6.0->langchain-huggingface) (3.0.2)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.11/dist-packages (from anyio->httpx<1,>=0.23.0->langsmith<0.4,>=0.1.125->langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (1.3.1)\n",
            "Downloading langchain_huggingface-0.1.2-py3-none-any.whl (21 kB)\n",
            "Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl (363.4 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (13.8 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m43.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (24.6 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m30.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (883 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m39.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl (664.8 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl (211.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl (56.3 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl (127.9 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m91.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, langchain-huggingface\n",
            "  Attempting uninstall: nvidia-nvjitlink-cu12\n",
            "    Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
            "    Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-curand-cu12\n",
            "    Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
            "    Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
            "      Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
            "  Attempting uninstall: nvidia-cufft-cu12\n",
            "    Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
            "    Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
            "      Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
            "  Attempting uninstall: nvidia-cuda-runtime-cu12\n",
            "    Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
            "    Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cuda-cupti-cu12\n",
            "    Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cublas-cu12\n",
            "    Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
            "    Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
            "      Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
            "  Attempting uninstall: nvidia-cusparse-cu12\n",
            "    Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
            "    Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
            "      Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
            "  Attempting uninstall: nvidia-cudnn-cu12\n",
            "    Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
            "    Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
            "      Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
            "  Attempting uninstall: nvidia-cusolver-cu12\n",
            "    Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
            "    Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
            "      Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
            "Successfully installed langchain-huggingface-0.1.2 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.11/dist-packages (13.9.4)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from rich) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.11/dist-packages (from rich) (2.18.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.11/dist-packages (from markdown-it-py>=2.2.0->rich) (0.1.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install -qU langchain-google-vertexai\n",
        "!pip install -qU langchain-core\n",
        "!pip install langchain_community\n",
        "!pip install langchain-huggingface\n",
        "!pip install rich"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from rich.console import Console\n",
        "console = Console()"
      ],
      "metadata": {
        "id": "KDrzM8sHW_Oe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import auth\n",
        "auth.authenticate_user()"
      ],
      "metadata": {
        "id": "Sxao7S0ASJUd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Getting Whisper Model**"
      ],
      "metadata": {
        "id": "0EzHGTcnXJsC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "pipe = pipeline(\"automatic-speech-recognition\", model=\"openai/whisper-large-v3\")"
      ],
      "metadata": {
        "id": "A42Lhp2jSLOt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386,
          "referenced_widgets": [
            "7cd27bf2605d4f42a578ff5cea830268",
            "42866915ce1d4ecb92a92567365cdc11",
            "6549f64b6568400d836d88075936e970",
            "e60ed5097ec342db9bb32a1167b68647",
            "43851139096f4d85b7f805279219312f",
            "9121241c059f4804bce3446c08e37dae",
            "2b27930b72014d00b1b4d3b337f1e561",
            "aee1444e309741dfb9ae4811bea23c04",
            "abd2740362ba44a1b56933c8c975a0dd",
            "713e0f83abc444e0b42bb23eac61d68f",
            "27b857a8bacf425a876d5d1157208ee9",
            "e3f216ceb60a450fa771a1c01b8195b7",
            "10e13b909a3646808d6fdfa267cd963a",
            "2a1dfc3b870f4af78316844454e0135b",
            "5191a002d4e14bfcb6add27bccef5561",
            "43ce9fa01b644e9fa644895945694c92",
            "f94cdc83e7444aed8885382b209d7ee8",
            "03f0c117b481489881450c1208c0dcba",
            "0213d1a4472b41c8ac046fb3b3bb19e9",
            "1304b646f28a46f4853941015c7c3ae1",
            "4a1a3d54644343feb8105afed3779f00",
            "05eca1c841e3439a929d258509236528",
            "60c615afb4ea46c98bc3e777282c85de",
            "2a49d49a36bc43afa89380ea6c4c698b",
            "800e623399ac4337978123a212bcb04a",
            "8869bb534f72474782851da11a8bb712",
            "13d6d8d9214c4da093d50a95438be301",
            "1959466725a146aa889ac0a2dd1a561b",
            "cc141014cf524d208645eb86c1b43333",
            "4adae18837714f1b87ddb90cd80206e8",
            "274a673ba8804ec4afb641161c6d34bf",
            "d4696ec30cb84aa5b52d676192192c86",
            "880ffdc7c9ab4c408d8ed782b4b202a4",
            "d58d5747eda543799ea2ee843810adbf",
            "fab44c82bd474f2f9f5b68cb3ea1e4c8",
            "f71a60bcf5834c3bbd679349b7c08cf2",
            "f101840a3b0b4717a11e9050330bcd9d",
            "29c541f3d2844fb1ba3b1b47444010cf",
            "1e5e20492c324e0081e36b7db140894b",
            "98cba063fa9844e299e4139568beccdf",
            "a70fea994b68491dbba514afb56dd137",
            "188e9962e93a43749b65dc58042dfe90",
            "ab6e19eafaa24094ac2dc2059aa4970a",
            "a15997e2b5114e818ec5add42e0298a8",
            "d58171a5916240eeb2869cd662207efa",
            "f75954f133234ffbbc19b921f9fd1eeb",
            "7e8d8fcf9a514048bb4f5678957eca01",
            "f08b456336d0487a865d3880a2b1242f",
            "96d783870fa44526b7de0009babc22eb",
            "bd359b1a906b483191dd04459c82d90d",
            "b929e29b54cc4738a033950ef7419747",
            "8e72ce788f524c208a95057f55f4bcbc",
            "ca619e3779284cab8dd706e8ac525c7a",
            "88a2cd22959147239e0c8453d21eb407",
            "e162653cbd4140719f8f0d3b974c182f",
            "fa4d050acb164f4aac7306251ec18302",
            "13841b77c0d248ce947479f2c8f9ed10",
            "7611800d2f9d426da92fead76ed47b05",
            "eabc39a733b34174a2b2c4fb267d6edd",
            "261e80d067a24b489c2a88ee1f8d5616",
            "32a1ede9675c477087ad7dcea2ef76cf",
            "e54a89d7d22a413a985a806c1c0dc6f2",
            "5418a71eafe3460c8ecda807dc0c9c81",
            "3462975448cc47448f039e41dbcb5ac2",
            "8a24ee76c6b7454a9e830a57c4d67565",
            "0dcd3125a61545a4a8a66fc5d3b7bff1",
            "7efcc6e1b77b4265a35615c146010a86",
            "ebd973d8b25c4623bfa9c6e1cb9b9781",
            "6271d077a7f74d049c89724462d0b5b5",
            "cc6b89b4d64a4067a138bfa1d8f6ebd4",
            "003921a693be4a37885f67641532886c",
            "cf355d877aa6454b8e30f576995c0e28",
            "5152cc38021a488ea935f2bfe6bb23fb",
            "9317c8d548f846e4b46a9bb295ff5bae",
            "fa22a0cf533e4f79a03c5caf38a5bc37",
            "12b347d6ba894f18a817ea698e376cc4",
            "338893f3233247789fe3a03e1d7fdb51",
            "35ffefe926364c69a6517be47cf88898",
            "873dafff73ff4236aea46488eed1c1d7",
            "86b3be79c7c44cdab1dc56a1dfff9bdb",
            "f2e36274161a473b83b448fd83bee495",
            "6cf5ebb8300146fd8b12b0f426796503",
            "0949279f3d7e4b9d83fcf9e63384f063",
            "9118aa8b14654bf6811971f377d0110b",
            "ae9b5e020fb64d1dab1dca8d7580dbef",
            "2fc411a517394b34af8ffb9b47f23f83",
            "34a10dd1ab6845419d2d87b93dbebaf2",
            "f0cb4a21e73744488493fa81625c7f8c",
            "532e6b5d6387467fb4e8db0f1ce74407",
            "dcad3ebcf1fc4aebb14e1a966463fe9d",
            "ce47b5579e7a4df7b02c92158ec68c35",
            "b50fa1a08c4e48cfaa1c93bee498812d",
            "e6014d4864164bca84c39162f8fec64f",
            "bdc553ab99ac41f2865a4aa2bb81b57c",
            "a23cbac25a1b432ea7443e09f2a5e839",
            "cc30d9b6e91541678afcfa5c1bdb8401",
            "431661cb99da4cbe9b5d86517f3b85f5",
            "584f98c515604e5f831c18dc40106807",
            "1bc68202fe81403283658a382f9e7229",
            "3a1c454f2a58409996901acb1291ffd6",
            "f932c4a5b78b441fae6c2df051cfd34b",
            "18f696bb727049d3937e2fa70f453ae2",
            "2ed9357ef5464c3496d29bc388283fa3",
            "bfa87a4891e94d31b5379fc601df83dc",
            "ef1e00e7f8184df1b97ca95de9adfc5f",
            "b3d6976a6dc8400cb5b93d828f12bcc1",
            "006ea14b941c4107bd37ef1f3bf4fe50",
            "5ed9486fa4f9406990f9021aae794dcc",
            "3a86142e8eb6413699670116513b49af",
            "8b9206dcb6264dff846448874dee48e8",
            "48768421138f454d9f01a8fd354830d0",
            "7b879d2278f24d11809cf0e5218368f7",
            "e8284eb2b93c43b2abce65c1526e1056",
            "9ae2788af6cf4efd8e367da6b8d2833b",
            "252d4ec8aade409da054544dfb352341",
            "89c7010a422642c2bf963a131abd9c0e",
            "6cc96f4f01f84c2aa1f5fcf7191f7c17",
            "9a463457254043b99b9f2df1cdf90c7d",
            "1723a47885ad433c85f24d54fe07ef2a",
            "f3756bbd97804f7d86f6187f935b91a1",
            "8fccc011526f4fc2be92657b6fdea816"
          ]
        },
        "outputId": "ce99d00a-1b58-420b-c7a1-69b41852f355"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.27k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7cd27bf2605d4f42a578ff5cea830268"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/3.09G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e3f216ceb60a450fa771a1c01b8195b7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/3.90k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "60c615afb4ea46c98bc3e777282c85de"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/283k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d58d5747eda543799ea2ee843810adbf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "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": "d58171a5916240eeb2869cd662207efa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.48M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fa4d050acb164f4aac7306251ec18302"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/494k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7efcc6e1b77b4265a35615c146010a86"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "normalizer.json:   0%|          | 0.00/52.7k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "35ffefe926364c69a6517be47cf88898"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/34.6k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "532e6b5d6387467fb4e8db0f1ce74407"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/2.07k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3a1c454f2a58409996901acb1291ffd6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/340 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "48768421138f454d9f01a8fd354830d0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Device set to use cuda:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trad = pipe(\"introagents.mp4\", return_timestamps=True)\n",
        "console.print(trad)"
      ],
      "metadata": {
        "id": "Blwt_T5cSNki",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "43aded10-090a-4c64-b673-53fbc58b2ba6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/transformers/models/whisper/generation_whisper.py:512: FutureWarning: The input name `inputs` is deprecated. Please make sure to use `input_features` instead.\n",
            "  warnings.warn(\n",
            "Due to a bug fix in https://github.com/huggingface/transformers/pull/28687 transcription using a multilingual Whisper will default to language detection followed by transcription instead of translation to English.This might be a breaking change for your use case. If you want to instead always translate your audio to English, make sure to pass `language='en'`.\n",
            "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.43.0. You should pass an instance of `EncoderDecoderCache` instead, e.g. `past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m{\u001b[0m\n",
              "    \u001b[32m'text'\u001b[0m: \u001b[32m\" Hey Jason, what's today's topic? Agents. Good one. So I hear a lot of folks talking about agents and \u001b[0m\n",
              "\u001b[32magentic architectures, but let's back up a bit. What's an agent? Well, that's the fun part. No one seems to agree \u001b[0m\n",
              "\u001b[32mwhat exactly an agent is. So in my mental model, an agent is an AI with a job. and the tools necessary to do that \u001b[0m\n",
              "\u001b[32mjob. I've referred to them before as like AI microservices or tiny little AI apps. How do you think about them? You\u001b[0m\n",
              "\u001b[32mmean something like a customer service agent? I always try to think of things in this like I would something in the\u001b[0m\n",
              "\u001b[32mreal world. An agent should be something that helps me to get something done more effectively, get it done better, \u001b[0m\n",
              "\u001b[32mor to accomplish something I wouldn't have been able to do otherwise, like a customer service agent helping me. So \u001b[0m\n",
              "\u001b[32msome folks say that agents must act autonomously, but acting autonomous is also kind of vague. If an agent uses \u001b[0m\n",
              "\u001b[32mtools to research a topic and then summarizes the results for you, is that acting autonomously? Other people say \u001b[0m\n",
              "\u001b[32mthat agents must be able to reason about their environment based on the inputs and based on their own outputs. And \u001b[0m\n",
              "\u001b[32mI wrote in one place that an agent was a system that was prompted an LLM more than once before returning to the \u001b[0m\n",
              "\u001b[32muser. I think we can agree that the definitions vary wildly. So how can we talk about agents without having a clear\u001b[0m\n",
              "\u001b[32midea about what they are. So Andrew Ng wrote a blog post about this challenge and suggested that instead of talking\u001b[0m\n",
              "\u001b[32mabout things as agent versus not agent, we just acknowledge that different kinds of systems can be agentic to \u001b[0m\n",
              "\u001b[32mdifferent degrees. Okay. So an AI completing a task autonomously is agentic, and an AI researching and summarizing \u001b[0m\n",
              "\u001b[32mfor you is also agentic, but maybe not to the same kind of degree of agentic. Yes. So to better appreciate the full\u001b[0m\n",
              "\u001b[32mrange of agenticness, let's do a quick speed round of all the different use cases for agents. Go first. All right. \u001b[0m\n",
              "\u001b[32mAn agent that could help me review my emails and create tasks on a digital to-do list based on the highest priority\u001b[0m\n",
              "\u001b[32memails that come in. Or an agent that could use weather data and other information to decide when to water my \u001b[0m\n",
              "\u001b[32mgarden and how long to water it for. Or an agent that could respond to people reporting bugs and automatically \u001b[0m\n",
              "\u001b[32mgather missing information and seek clarification on confusing parts of a bug report, and then route the bug to the\u001b[0m\n",
              "\u001b[32mappropriate team to resolve. Or an agent could use a camera to look at a Rubik's cube and then use robotic \u001b[0m\n",
              "\u001b[32mmanipulators to solve it. Do I get any fun ones yet? Oh, here we go. I could use an agent also to build a travel \u001b[0m\n",
              "\u001b[32mplanning agent that takes my preferences and needs into account, researches things like destinations and hotels, \u001b[0m\n",
              "\u001b[32mand even activities, and then suggests an itinerary for me, and use a collaborating agent to book everything all in\u001b[0m\n",
              "\u001b[32mone shot. Or I could build an agent that suggests an outfit for every day based on the weather and what's currently\u001b[0m\n",
              "\u001b[32mclean in my closet. All right, I think we're getting some pretty cool examples here for folks to work with. By the \u001b[0m\n",
              "\u001b[32mway, if you're looking for code for some of the agents that we've actually discussed here, these are available from\u001b[0m\n",
              "\u001b[32mGoogle, and we've put the links to that code in the description below. I think we've seen that there are lots of \u001b[0m\n",
              "\u001b[32mdifferent types of agents, and it's more important to think about things being agentic than it is a specific \u001b[0m\n",
              "\u001b[32mdefinition about an agent or how to build it. But since we want to talk about building specifically, what are some \u001b[0m\n",
              "\u001b[32mthings I should consider when I'm building an agent? So I think one of the most important things to remember is \u001b[0m\n",
              "\u001b[32mthat an agent doesn't necessarily need to even incorporate an LLM. If your agent doesn't need generative AI \u001b[0m\n",
              "\u001b[32mcapabilities, you probably don't need an LLM. You may be able to use a simple hard-coded algorithm to achieve your \u001b[0m\n",
              "\u001b[32mgoal. That actually makes sense. Agents were around before LLMs took off and either used hard-coded rules or used \u001b[0m\n",
              "\u001b[32mcustom-trained models things like BERT. I remember doing this as a part of robotic process automation bots even \u001b[0m\n",
              "\u001b[32mjust a few years ago. And those options, and more importantly the use cases for those agents, are even more valid \u001b[0m\n",
              "\u001b[32mtoday than they were five years ago. Exactly. And if you do use an LLM, you can use tools like function calling to \u001b[0m\n",
              "\u001b[32massist your agent in completing a task. You can also give your agent the ability to retrieve data from data \u001b[0m\n",
              "\u001b[32mstorage, so you can use techniques that we've talked about already. Alright, so we've been talking about agents as \u001b[0m\n",
              "\u001b[32mif they're single entities. But I think agents can work together, right? One way we can think about agents is that \u001b[0m\n",
              "\u001b[32mwe could have different agents with different functionalities or different tools, and the agents can even interact \u001b[0m\n",
              "\u001b[32mwith each other to accomplish a goal. For example, maybe I would have a shipping agent and a customer service agent\u001b[0m\n",
              "\u001b[32mthat can interact to figure out why a package never arrived for a customer. You could have these agents working \u001b[0m\n",
              "\u001b[32mtogether also to improve the output. Maybe you have one agent that produces blog posts and a second agent that \u001b[0m\n",
              "\u001b[32mcritiques them for style and technical accuracy. You can create a loop where the critical agent feedback goes to \u001b[0m\n",
              "\u001b[32mthe producer agent, and they go back and forth refining the blog post until the critic agent doesn't find any more \u001b[0m\n",
              "\u001b[32missues. So if I wanted to get started building agents, is there a best place to begin? It does really depend on \u001b[0m\n",
              "\u001b[32myour particular agent. A very simple agent could just be some business logic with an interface to enable \u001b[0m\n",
              "\u001b[32minteraction with the environment or with the user. And since we're talking about degrees of agentic behavior, some \u001b[0m\n",
              "\u001b[32mprompts and business logic packaged together could also be an agent. There are some frameworks for helping you \u001b[0m\n",
              "\u001b[32mbuild agents, and if you want some guidance about this, they have more things around architecture and orchestration\u001b[0m\n",
              "\u001b[32mand even build. Also, general purpose frameworks for interacting with models can be used to build agents, \u001b[0m\n",
              "\u001b[32mespecially agents that are designed around a goal and some tools to achieve that specific goal. So if you'd like to\u001b[0m\n",
              "\u001b[32mtry building an agent yourself, we've included some links to tutorials using different approaches and different \u001b[0m\n",
              "\u001b[32mdegrees of agenticness. And we also have a tutorial for building agents and getting started with Vertex AI Agent \u001b[0m\n",
              "\u001b[32mBuilder. And there's even one more tutorial on building a vacation planning agent using Firebase and Genkit. Lots \u001b[0m\n",
              "\u001b[32mof options. Can't wait to see what you build. Happy prompting, everyone.\"\u001b[0m,\n",
              "    \u001b[32m'chunks'\u001b[0m: \u001b[1m[\u001b[0m\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m0.0\u001b[0m, \u001b[1;36m11.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m\" Hey Jason, what's today's topic?\"\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m11.0\u001b[0m, \u001b[1;36m12.5\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' Agents.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m12.5\u001b[0m, \u001b[1;36m13.32\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' Good one.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m13.32\u001b[0m, \u001b[1;36m15.84\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' So I hear a lot of folks talking about agents'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m15.84\u001b[0m, \u001b[1;36m19.5\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m\" and agentic architectures, but let's back up a bit.\"\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m19.5\u001b[0m, \u001b[1;36m20.56\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m\" What's an agent?\"\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m20.56\u001b[0m, \u001b[1;36m22.48\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m\" Well, that's the fun part.\"\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m22.48\u001b[0m, \u001b[1;36m25.44\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' No one seems to agree what exactly an agent is.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m25.44\u001b[0m, \u001b[1;36m29.6\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' So in my mental model, an agent is an AI with a job.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m29.6\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m''\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m2.38\u001b[0m, \u001b[1;36m5.32\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m\" and the tools necessary to do that job. I've referred to them before as like AI \u001b[0m\n",
              "\u001b[32mmicroservices\"\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m5.32\u001b[0m, \u001b[1;36m7.82\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' or tiny little AI apps.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m7.82\u001b[0m, \u001b[1;36m9.24\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' How do you think about them?'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m9.24\u001b[0m, \u001b[1;36m11.32\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' You mean something like a customer service agent?'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m11.32\u001b[0m, \u001b[1;36m13.22\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' I always try to think of things in this'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m13.22\u001b[0m, \u001b[1;36m15.12\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' like I would something in the real world.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m15.12\u001b[0m, \u001b[1;36m16.74\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' An agent should be something that helps me'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m16.74\u001b[0m, \u001b[1;36m18.66\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' to get something done more effectively,'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m18.66\u001b[0m, \u001b[1;36m20.06\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' get it done better,'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m20.06\u001b[0m, \u001b[1;36m21.18\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' or to accomplish something'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m21.18\u001b[0m, \u001b[1;36m23.38\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m\" I wouldn't have been able to do otherwise,\"\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m23.38\u001b[0m, \u001b[1;36m25.42\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' like a customer service agent helping me.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m25.42\u001b[0m, \u001b[1;36m28.9\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' So some folks say that agents must act autonomously,'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m28.9\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m''\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m3.62\u001b[0m, \u001b[1;36m5.92\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m' but acting autonomous is also kind of vague. If an agent uses tools to research a topic'\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m5.92\u001b[0m, \u001b[1;36m7.66\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' and then summarizes the results for you,'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m7.66\u001b[0m, \u001b[1;36m9.22\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' is that acting autonomously?'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m9.22\u001b[0m, \u001b[1;36m12.08\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' Other people say that agents must be able to reason'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m12.08\u001b[0m, \u001b[1;36m14.28\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' about their environment based on the inputs'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m14.28\u001b[0m, \u001b[1;36m16.28\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' and based on their own outputs.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m16.28\u001b[0m, \u001b[1;36m18.62\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' And I wrote in one place that an agent was a system'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m18.62\u001b[0m, \u001b[1;36m20.96\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' that was prompted an LLM more than once'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m20.96\u001b[0m, \u001b[1;36m22.4\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' before returning to the user.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m22.4\u001b[0m, \u001b[1;36m25.7\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' I think we can agree that the definitions vary wildly.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m25.7\u001b[0m, \u001b[1;36m27.26\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' So how can we talk about agents'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m27.26\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m''\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m2.96\u001b[0m, \u001b[1;36m5.78\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m' without having a clear idea about what they are. So Andrew Ng wrote a blog post about this \u001b[0m\n",
              "\u001b[32mchallenge'\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m5.78\u001b[0m, \u001b[1;36m8.24\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' and suggested that instead of talking about things'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m8.24\u001b[0m, \u001b[1;36m10.66\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' as agent versus not agent,'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m10.66\u001b[0m, \u001b[1;36m13.02\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' we just acknowledge that different kinds of systems'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m13.02\u001b[0m, \u001b[1;36m15.92\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' can be agentic to different degrees.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m15.92\u001b[0m, \u001b[1;36m16.98\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' Okay.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m16.98\u001b[0m, \u001b[1;36m20.68\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' So an AI completing a task autonomously is agentic,'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m20.68\u001b[0m, \u001b[1;36m23.06\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' and an AI researching and summarizing for you'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m23.06\u001b[0m, \u001b[1;36m24.6\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' is also agentic,'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m24.6\u001b[0m, \u001b[1;36m27.74\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' but maybe not to the same kind of degree of agentic.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m27.74\u001b[0m, \u001b[1;36m28.76\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' Yes.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m28.76\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m''\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m3.84\u001b[0m, \u001b[1;36m6.32\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m\" So to better appreciate the full range of agenticness, let's do a quick speed round of all \u001b[0m\n",
              "\u001b[32mthe different use\"\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m6.32\u001b[0m, \u001b[1;36m7.62\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' cases for agents.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m7.62\u001b[0m, \u001b[1;36m8.46\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' Go first.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m8.46\u001b[0m, \u001b[1;36m9.18\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' All right.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m9.18\u001b[0m, \u001b[1;36m11.56\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' An agent that could help me review my emails'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m11.56\u001b[0m, \u001b[1;36m14.04\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' and create tasks on a digital to-do list'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m14.04\u001b[0m, \u001b[1;36m16.44\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' based on the highest priority emails that come in.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m16.44\u001b[0m, \u001b[1;36m18.22\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' Or an agent that could use weather data'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m18.22\u001b[0m, \u001b[1;36m20.78\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' and other information to decide when to water my garden'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m20.78\u001b[0m, \u001b[1;36m22.5\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' and how long to water it for.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m22.5\u001b[0m, \u001b[1;36m25.8\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' Or an agent that could respond to people reporting bugs'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m25.8\u001b[0m, \u001b[1;36m28.36\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' and automatically gather missing information'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m28.36\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m''\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m2.94\u001b[0m, \u001b[1;36m5.58\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m' and seek clarification on confusing parts of a bug report, and then route the bug to the \u001b[0m\n",
              "\u001b[32mappropriate team to resolve.'\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m5.58\u001b[0m, \u001b[1;36m8.64\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m\" Or an agent could use a camera to look at a Rubik's cube\"\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m8.64\u001b[0m, \u001b[1;36m10.96\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' and then use robotic manipulators to solve it.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m10.96\u001b[0m, \u001b[1;36m12.08\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' Do I get any fun ones yet?'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m12.08\u001b[0m, \u001b[1;36m12.92\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' Oh, here we go.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m12.92\u001b[0m, \u001b[1;36m15.92\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' I could use an agent also to build a travel planning agent'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m15.92\u001b[0m, \u001b[1;36m18.64\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' that takes my preferences and needs into account,'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m18.64\u001b[0m, \u001b[1;36m21.44\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' researches things like destinations and hotels,'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m21.44\u001b[0m, \u001b[1;36m25.08\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' and even activities, and then suggests an itinerary for me,'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m25.08\u001b[0m, \u001b[1;36m27.74\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' and use a collaborating agent to book everything'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m27.74\u001b[0m, \u001b[1;36m28.72\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' all in one shot.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m28.72\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m''\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m2.7\u001b[0m, \u001b[1;36m4.68\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m' Or I could build an agent that suggests an outfit for every day based on the weather'\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m4.68\u001b[0m, \u001b[1;36m6.46\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m\" and what's currently clean in my closet.\"\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m6.46\u001b[0m, \u001b[1;36m9.22\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m\" All right, I think we're getting some pretty cool examples\"\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m9.22\u001b[0m, \u001b[1;36m10.42\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' here for folks to work with.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m10.42\u001b[0m, \u001b[1;36m12.4\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m\" By the way, if you're looking for code\"\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m12.4\u001b[0m, \u001b[1;36m14.66\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m\" for some of the agents that we've actually discussed here,\"\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m14.66\u001b[0m, \u001b[1;36m16.4\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' these are available from Google,'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m16.4\u001b[0m, \u001b[1;36m17.94\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m\" and we've put the links to that code\"\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m17.94\u001b[0m, \u001b[1;36m19.24\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' in the description below.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m19.24\u001b[0m, \u001b[1;36m20.84\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m\" I think we've seen that there are lots\"\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m20.84\u001b[0m, \u001b[1;36m22.7\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' of different types of agents,'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m22.7\u001b[0m, \u001b[1;36m25.54\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m\" and it's more important to think about things being agentic\"\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m25.54\u001b[0m, \u001b[1;36m28.22\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' than it is a specific definition about an agent'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m28.22\u001b[0m, \u001b[1;36m29.14\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' or how to build it.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m29.14\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m''\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m2.68\u001b[0m, \u001b[1;36m2.96\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' But since we want to talk about building specifically,'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m5.44\u001b[0m, \u001b[1;36m5.66\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m\" what are some things I should consider when I'm building an agent?\"\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m8.46\u001b[0m, \u001b[1;36m13.08\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m\" So I think one of the most important things to remember is that an agent doesn't necessarily \u001b[0m\n",
              "\u001b[32mneed to even incorporate an LLM.\"\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m13.08\u001b[0m, \u001b[1;36m15.72\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m\" If your agent doesn't need generative AI capabilities,\"\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m16.04\u001b[0m, \u001b[1;36m17.94\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m\" you probably don't need an LLM.\"\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m18.14\u001b[0m, \u001b[1;36m21.76\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m' You may be able to use a simple hard-coded algorithm to achieve your goal.'\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m22.24\u001b[0m, \u001b[1;36m23.54\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' That actually makes sense.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m23.86\u001b[0m, \u001b[1;36m26.06\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' Agents were around before LLMs took off'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m26.06\u001b[0m, \u001b[1;36m29.9\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' and either used hard-coded rules or used custom-trained models'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m29.9\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m''\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m4.18\u001b[0m, \u001b[1;36m8.24\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m' things like BERT. I remember doing this as a part of robotic process automation bots even \u001b[0m\n",
              "\u001b[32mjust a few years ago. And those options, and more importantly the use'\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m8.24\u001b[0m, \u001b[1;36m12.3\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m' cases for those agents, are even more valid today than they were five years'\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m12.3\u001b[0m, \u001b[1;36m17.16\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m' ago. Exactly. And if you do use an LLM, you can use tools like function calling to'\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m17.16\u001b[0m, \u001b[1;36m20.88\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m' assist your agent in completing a task. You can also give your agent the ability'\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m20.88\u001b[0m, \u001b[1;36m23.74\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m\" to retrieve data from data storage, so you can use techniques that we've talked\"\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m23.74\u001b[0m, \u001b[1;36m26.7\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m\" about already. Alright, so we've been talking about agents as if they're\"\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m26.7\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m''\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m4.62\u001b[0m, \u001b[1;36m9.22\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m' single entities. But I think agents can work together, right? One way we can think about \u001b[0m\n",
              "\u001b[32magents is that we could have different agents with different functionalities or different tools,'\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m9.22\u001b[0m, \u001b[1;36m14.42\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m' and the agents can even interact with each other to accomplish a goal. For example, maybe I \u001b[0m\n",
              "\u001b[32mwould'\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m14.42\u001b[0m, \u001b[1;36m19.54\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m' have a shipping agent and a customer service agent that can interact to figure out why a \u001b[0m\n",
              "\u001b[32mpackage'\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m19.54\u001b[0m, \u001b[1;36m24.22\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m' never arrived for a customer. You could have these agents working together also to improve \u001b[0m\n",
              "\u001b[32mthe output.'\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m24.7\u001b[0m, \u001b[1;36m29.48\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m' Maybe you have one agent that produces blog posts and a second agent that critiques them for \u001b[0m\n",
              "\u001b[32mstyle'\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m29.48\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m''\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m1.22\u001b[0m, \u001b[1;36m1.74\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' and technical accuracy.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m4.0\u001b[0m, \u001b[1;36m5.54\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m' You can create a loop where the critical agent feedback goes to the producer agent,'\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m5.98\u001b[0m, \u001b[1;36m8.22\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' and they go back and forth refining the blog post'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m8.22\u001b[0m, \u001b[1;36m10.94\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m\" until the critic agent doesn't find any more issues.\"\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m11.34\u001b[0m, \u001b[1;36m13.92\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' So if I wanted to get started building agents,'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m14.08\u001b[0m, \u001b[1;36m15.36\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' is there a best place to begin?'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m15.68\u001b[0m, \u001b[1;36m17.84\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' It does really depend on your particular agent.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m18.16\u001b[0m, \u001b[1;36m20.52\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' A very simple agent could just be some business logic'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m20.52\u001b[0m, \u001b[1;36m22.46\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' with an interface to enable interaction'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m22.46\u001b[0m, \u001b[1;36m24.3\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' with the environment or with the user.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m24.58\u001b[0m, \u001b[1;36m27.16\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m\" And since we're talking about degrees of agentic behavior,\"\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m27.74\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m''\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m4.5\u001b[0m, \u001b[1;36m7.82\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m' some prompts and business logic packaged together could also be an agent. There are some \u001b[0m\n",
              "\u001b[32mframeworks for helping you build agents, and if you want some'\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m7.82\u001b[0m, \u001b[1;36m11.1\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m' guidance about this, they have more things around architecture and'\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m11.1\u001b[0m, \u001b[1;36m14.82\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m' orchestration and even build. Also, general purpose frameworks for'\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m14.82\u001b[0m, \u001b[1;36m18.68\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m' interacting with models can be used to build agents, especially agents that are'\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m18.68\u001b[0m, \u001b[1;36m22.8\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m\" designed around a goal and some tools to achieve that specific goal. So if you'd\"\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m22.8\u001b[0m, \u001b[1;36m26.22\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m\" like to try building an agent yourself, we've included some links to tutorials\"\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m26.22\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m''\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m3.38\u001b[0m, \u001b[1;36m6.04\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m' using different approaches and different degrees of agenticness. And we also have a tutorial \u001b[0m\n",
              "\u001b[32mfor building agents'\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m6.04\u001b[0m, \u001b[1;36m8.54\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' and getting started with Vertex AI Agent Builder.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\n",
              "            \u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m8.54\u001b[0m, \u001b[1;36m11.88\u001b[0m\u001b[1m)\u001b[0m,\n",
              "            \u001b[32m'text'\u001b[0m: \u001b[32m\" And there's even one more tutorial on building a vacation planning agent\"\u001b[0m\n",
              "        \u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m11.88\u001b[0m, \u001b[1;36m13.34\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' using Firebase and Genkit.'\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m13.34\u001b[0m, \u001b[1;36m15.94\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m\" Lots of options. Can't wait to see what you build.\"\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[1m{\u001b[0m\u001b[32m'timestamp'\u001b[0m: \u001b[1m(\u001b[0m\u001b[1;36m15.94\u001b[0m, \u001b[1;36m17.36\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'text'\u001b[0m: \u001b[32m' Happy prompting, everyone.'\u001b[0m\u001b[1m}\u001b[0m\n",
              "    \u001b[1m]\u001b[0m\n",
              "\u001b[1m}\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
              "    <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" Hey Jason, what's today's topic? Agents. Good one. So I hear a lot of folks talking about agents and </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">agentic architectures, but let's back up a bit. What's an agent? Well, that's the fun part. No one seems to agree </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">what exactly an agent is. So in my mental model, an agent is an AI with a job. and the tools necessary to do that </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">job. I've referred to them before as like AI microservices or tiny little AI apps. How do you think about them? You</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">mean something like a customer service agent? I always try to think of things in this like I would something in the</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">real world. An agent should be something that helps me to get something done more effectively, get it done better, </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">or to accomplish something I wouldn't have been able to do otherwise, like a customer service agent helping me. So </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">some folks say that agents must act autonomously, but acting autonomous is also kind of vague. If an agent uses </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">tools to research a topic and then summarizes the results for you, is that acting autonomously? Other people say </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">that agents must be able to reason about their environment based on the inputs and based on their own outputs. And </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">I wrote in one place that an agent was a system that was prompted an LLM more than once before returning to the </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">user. I think we can agree that the definitions vary wildly. So how can we talk about agents without having a clear</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">idea about what they are. So Andrew Ng wrote a blog post about this challenge and suggested that instead of talking</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">about things as agent versus not agent, we just acknowledge that different kinds of systems can be agentic to </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">different degrees. Okay. So an AI completing a task autonomously is agentic, and an AI researching and summarizing </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">for you is also agentic, but maybe not to the same kind of degree of agentic. Yes. So to better appreciate the full</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">range of agenticness, let's do a quick speed round of all the different use cases for agents. Go first. All right. </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">An agent that could help me review my emails and create tasks on a digital to-do list based on the highest priority</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">emails that come in. Or an agent that could use weather data and other information to decide when to water my </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">garden and how long to water it for. Or an agent that could respond to people reporting bugs and automatically </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">gather missing information and seek clarification on confusing parts of a bug report, and then route the bug to the</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">appropriate team to resolve. Or an agent could use a camera to look at a Rubik's cube and then use robotic </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">manipulators to solve it. Do I get any fun ones yet? Oh, here we go. I could use an agent also to build a travel </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">planning agent that takes my preferences and needs into account, researches things like destinations and hotels, </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">and even activities, and then suggests an itinerary for me, and use a collaborating agent to book everything all in</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">one shot. Or I could build an agent that suggests an outfit for every day based on the weather and what's currently</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">clean in my closet. All right, I think we're getting some pretty cool examples here for folks to work with. By the </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">way, if you're looking for code for some of the agents that we've actually discussed here, these are available from</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">Google, and we've put the links to that code in the description below. I think we've seen that there are lots of </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">different types of agents, and it's more important to think about things being agentic than it is a specific </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">definition about an agent or how to build it. But since we want to talk about building specifically, what are some </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">things I should consider when I'm building an agent? So I think one of the most important things to remember is </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">that an agent doesn't necessarily need to even incorporate an LLM. If your agent doesn't need generative AI </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">capabilities, you probably don't need an LLM. You may be able to use a simple hard-coded algorithm to achieve your </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">goal. That actually makes sense. Agents were around before LLMs took off and either used hard-coded rules or used </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">custom-trained models things like BERT. I remember doing this as a part of robotic process automation bots even </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">just a few years ago. And those options, and more importantly the use cases for those agents, are even more valid </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">today than they were five years ago. Exactly. And if you do use an LLM, you can use tools like function calling to </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">assist your agent in completing a task. You can also give your agent the ability to retrieve data from data </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">storage, so you can use techniques that we've talked about already. Alright, so we've been talking about agents as </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">if they're single entities. But I think agents can work together, right? One way we can think about agents is that </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">we could have different agents with different functionalities or different tools, and the agents can even interact </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">with each other to accomplish a goal. For example, maybe I would have a shipping agent and a customer service agent</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">that can interact to figure out why a package never arrived for a customer. You could have these agents working </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">together also to improve the output. Maybe you have one agent that produces blog posts and a second agent that </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">critiques them for style and technical accuracy. You can create a loop where the critical agent feedback goes to </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">the producer agent, and they go back and forth refining the blog post until the critic agent doesn't find any more </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">issues. So if I wanted to get started building agents, is there a best place to begin? It does really depend on </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">your particular agent. A very simple agent could just be some business logic with an interface to enable </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">interaction with the environment or with the user. And since we're talking about degrees of agentic behavior, some </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">prompts and business logic packaged together could also be an agent. There are some frameworks for helping you </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">build agents, and if you want some guidance about this, they have more things around architecture and orchestration</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">and even build. Also, general purpose frameworks for interacting with models can be used to build agents, </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">especially agents that are designed around a goal and some tools to achieve that specific goal. So if you'd like to</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">try building an agent yourself, we've included some links to tutorials using different approaches and different </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">degrees of agenticness. And we also have a tutorial for building agents and getting started with Vertex AI Agent </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">Builder. And there's even one more tutorial on building a vacation planning agent using Firebase and Genkit. Lots </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">of options. Can't wait to see what you build. Happy prompting, everyone.\"</span>,\n",
              "    <span style=\"color: #008000; text-decoration-color: #008000\">'chunks'</span>: <span style=\"font-weight: bold\">[</span>\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11.0</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" Hey Jason, what's today's topic?\"</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11.0</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12.5</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' Agents.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12.5</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13.32</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' Good one.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13.32</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">15.84</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' So I hear a lot of folks talking about agents'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">15.84</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">19.5</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" and agentic architectures, but let's back up a bit.\"</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">19.5</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20.56</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" What's an agent?\"</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20.56</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22.48</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" Well, that's the fun part.\"</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22.48</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">25.44</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' No one seems to agree what exactly an agent is.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">25.44</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29.6</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' So in my mental model, an agent is an AI with a job.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29.6</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">''</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.38</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5.32</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" and the tools necessary to do that job. I've referred to them before as like AI </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">microservices\"</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5.32</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7.82</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' or tiny little AI apps.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7.82</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9.24</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' How do you think about them?'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9.24</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11.32</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' You mean something like a customer service agent?'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11.32</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13.22</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' I always try to think of things in this'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13.22</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">15.12</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' like I would something in the real world.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">15.12</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16.74</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' An agent should be something that helps me'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16.74</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">18.66</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' to get something done more effectively,'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">18.66</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20.06</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' get it done better,'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20.06</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">21.18</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' or to accomplish something'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">21.18</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">23.38</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" I wouldn't have been able to do otherwise,\"</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">23.38</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">25.42</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' like a customer service agent helping me.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">25.42</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">28.9</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' So some folks say that agents must act autonomously,'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">28.9</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">''</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.62</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5.92</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' but acting autonomous is also kind of vague. If an agent uses tools to research a topic'</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5.92</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7.66</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' and then summarizes the results for you,'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7.66</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9.22</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' is that acting autonomously?'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9.22</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12.08</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' Other people say that agents must be able to reason'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12.08</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">14.28</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' about their environment based on the inputs'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">14.28</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16.28</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' and based on their own outputs.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16.28</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">18.62</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' And I wrote in one place that an agent was a system'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">18.62</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20.96</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' that was prompted an LLM more than once'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20.96</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22.4</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' before returning to the user.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22.4</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">25.7</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' I think we can agree that the definitions vary wildly.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">25.7</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">27.26</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' So how can we talk about agents'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">27.26</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">''</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.96</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5.78</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' without having a clear idea about what they are. So Andrew Ng wrote a blog post about this </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">challenge'</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5.78</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8.24</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' and suggested that instead of talking about things'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8.24</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10.66</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' as agent versus not agent,'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10.66</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13.02</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' we just acknowledge that different kinds of systems'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13.02</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">15.92</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' can be agentic to different degrees.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">15.92</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16.98</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' Okay.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16.98</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20.68</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' So an AI completing a task autonomously is agentic,'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20.68</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">23.06</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' and an AI researching and summarizing for you'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">23.06</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">24.6</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' is also agentic,'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">24.6</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">27.74</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' but maybe not to the same kind of degree of agentic.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">27.74</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">28.76</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' Yes.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">28.76</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">''</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.84</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6.32</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" So to better appreciate the full range of agenticness, let's do a quick speed round of all </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">the different use\"</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6.32</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7.62</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' cases for agents.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7.62</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8.46</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' Go first.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8.46</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9.18</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' All right.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9.18</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11.56</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' An agent that could help me review my emails'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11.56</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">14.04</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' and create tasks on a digital to-do list'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">14.04</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16.44</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' based on the highest priority emails that come in.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16.44</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">18.22</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' Or an agent that could use weather data'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">18.22</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20.78</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' and other information to decide when to water my garden'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20.78</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22.5</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' and how long to water it for.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22.5</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">25.8</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' Or an agent that could respond to people reporting bugs'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">25.8</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">28.36</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' and automatically gather missing information'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">28.36</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">''</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.94</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5.58</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' and seek clarification on confusing parts of a bug report, and then route the bug to the </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">appropriate team to resolve.'</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5.58</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8.64</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" Or an agent could use a camera to look at a Rubik's cube\"</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8.64</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10.96</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' and then use robotic manipulators to solve it.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10.96</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12.08</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' Do I get any fun ones yet?'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12.08</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12.92</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' Oh, here we go.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12.92</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">15.92</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' I could use an agent also to build a travel planning agent'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">15.92</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">18.64</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' that takes my preferences and needs into account,'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">18.64</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">21.44</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' researches things like destinations and hotels,'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">21.44</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">25.08</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' and even activities, and then suggests an itinerary for me,'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">25.08</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">27.74</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' and use a collaborating agent to book everything'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">27.74</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">28.72</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' all in one shot.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">28.72</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">''</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.7</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.68</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' Or I could build an agent that suggests an outfit for every day based on the weather'</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.68</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6.46</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" and what's currently clean in my closet.\"</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6.46</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9.22</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" All right, I think we're getting some pretty cool examples\"</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9.22</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10.42</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' here for folks to work with.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10.42</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12.4</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" By the way, if you're looking for code\"</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12.4</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">14.66</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" for some of the agents that we've actually discussed here,\"</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">14.66</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16.4</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' these are available from Google,'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16.4</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">17.94</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" and we've put the links to that code\"</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">17.94</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">19.24</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' in the description below.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">19.24</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20.84</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" I think we've seen that there are lots\"</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20.84</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22.7</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' of different types of agents,'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22.7</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">25.54</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" and it's more important to think about things being agentic\"</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">25.54</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">28.22</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' than it is a specific definition about an agent'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">28.22</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29.14</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' or how to build it.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29.14</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">''</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.68</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.96</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' But since we want to talk about building specifically,'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5.44</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5.66</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" what are some things I should consider when I'm building an agent?\"</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8.46</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13.08</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" So I think one of the most important things to remember is that an agent doesn't necessarily </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">need to even incorporate an LLM.\"</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13.08</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">15.72</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" If your agent doesn't need generative AI capabilities,\"</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16.04</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">17.94</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" you probably don't need an LLM.\"</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">18.14</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">21.76</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' You may be able to use a simple hard-coded algorithm to achieve your goal.'</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22.24</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">23.54</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' That actually makes sense.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">23.86</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">26.06</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' Agents were around before LLMs took off'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">26.06</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29.9</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' and either used hard-coded rules or used custom-trained models'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29.9</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">''</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.18</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8.24</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' things like BERT. I remember doing this as a part of robotic process automation bots even </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">just a few years ago. And those options, and more importantly the use'</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8.24</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12.3</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' cases for those agents, are even more valid today than they were five years'</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12.3</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">17.16</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' ago. Exactly. And if you do use an LLM, you can use tools like function calling to'</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">17.16</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20.88</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' assist your agent in completing a task. You can also give your agent the ability'</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20.88</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">23.74</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" to retrieve data from data storage, so you can use techniques that we've talked\"</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">23.74</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">26.7</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" about already. Alright, so we've been talking about agents as if they're\"</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">26.7</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">''</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.62</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9.22</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' single entities. But I think agents can work together, right? One way we can think about </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">agents is that we could have different agents with different functionalities or different tools,'</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9.22</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">14.42</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' and the agents can even interact with each other to accomplish a goal. For example, maybe I </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">would'</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">14.42</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">19.54</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' have a shipping agent and a customer service agent that can interact to figure out why a </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">package'</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">19.54</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">24.22</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' never arrived for a customer. You could have these agents working together also to improve </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">the output.'</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">24.7</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29.48</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' Maybe you have one agent that produces blog posts and a second agent that critiques them for </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">style'</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29.48</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">''</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.22</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.74</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' and technical accuracy.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.0</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5.54</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' You can create a loop where the critical agent feedback goes to the producer agent,'</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5.98</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8.22</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' and they go back and forth refining the blog post'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8.22</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10.94</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" until the critic agent doesn't find any more issues.\"</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11.34</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13.92</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' So if I wanted to get started building agents,'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">14.08</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">15.36</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' is there a best place to begin?'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">15.68</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">17.84</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' It does really depend on your particular agent.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">18.16</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20.52</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' A very simple agent could just be some business logic'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20.52</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22.46</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' with an interface to enable interaction'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22.46</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">24.3</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' with the environment or with the user.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">24.58</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">27.16</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" And since we're talking about degrees of agentic behavior,\"</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">27.74</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">''</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.5</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7.82</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' some prompts and business logic packaged together could also be an agent. There are some </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">frameworks for helping you build agents, and if you want some'</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7.82</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11.1</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' guidance about this, they have more things around architecture and'</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11.1</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">14.82</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' orchestration and even build. Also, general purpose frameworks for'</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">14.82</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">18.68</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' interacting with models can be used to build agents, especially agents that are'</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">18.68</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22.8</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" designed around a goal and some tools to achieve that specific goal. So if you'd\"</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22.8</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">26.22</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" like to try building an agent yourself, we've included some links to tutorials\"</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">26.22</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">''</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.38</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6.04</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' using different approaches and different degrees of agenticness. And we also have a tutorial </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">for building agents'</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6.04</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8.54</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' and getting started with Vertex AI Agent Builder.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span>\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8.54</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11.88</span><span style=\"font-weight: bold\">)</span>,\n",
              "            <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" And there's even one more tutorial on building a vacation planning agent\"</span>\n",
              "        <span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11.88</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13.34</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' using Firebase and Genkit.'</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13.34</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">15.94</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\" Lots of options. Can't wait to see what you build.\"</span><span style=\"font-weight: bold\">}</span>,\n",
              "        <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'timestamp'</span>: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">15.94</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">17.36</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'text'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">' Happy prompting, everyone.'</span><span style=\"font-weight: bold\">}</span>\n",
              "    <span style=\"font-weight: bold\">]</span>\n",
              "<span style=\"font-weight: bold\">}</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = trad[\"text\"]\n",
        "console.print(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 966
        },
        "collapsed": true,
        "id": "1xXses5lo_mi",
        "outputId": "a5225ae0-aaea-4e71-8fc9-934240f20157"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              " Hey Jason, what's today's topic? Agents. Good one. So I hear a lot of folks talking about agents and agentic \n",
              "architectures, but let's back up a bit. What's an agent? Well, that's the fun part. No one seems to agree what \n",
              "exactly an agent is. So in my mental model, an agent is an AI with a job. and the tools necessary to do that job. \n",
              "I've referred to them before as like AI microservices or tiny little AI apps. How do you think about them? You mean\n",
              "something like a customer service agent? I always try to think of things in this like I would something in the real\n",
              "world. An agent should be something that helps me to get something done more effectively, get it done better, or to\n",
              "accomplish something I wouldn't have been able to do otherwise, like a customer service agent helping me. So some \n",
              "folks say that agents must act autonomously, but acting autonomous is also kind of vague. If an agent uses tools to\n",
              "research a topic and then summarizes the results for you, is that acting autonomously? Other people say that agents\n",
              "must be able to reason about their environment based on the inputs and based on their own outputs. And I wrote in \n",
              "one place that an agent was a system that was prompted an LLM more than once before returning to the user. I think \n",
              "we can agree that the definitions vary wildly. So how can we talk about agents without having a clear idea about \n",
              "what they are. So Andrew Ng wrote a blog post about this challenge and suggested that instead of talking about \n",
              "things as agent versus not agent, we just acknowledge that different kinds of systems can be agentic to different \n",
              "degrees. Okay. So an AI completing a task autonomously is agentic, and an AI researching and summarizing for you is\n",
              "also agentic, but maybe not to the same kind of degree of agentic. Yes. So to better appreciate the full range of \n",
              "agenticness, let's do a quick speed round of all the different use cases for agents. Go first. All right. An agent \n",
              "that could help me review my emails and create tasks on a digital to-do list based on the highest priority emails \n",
              "that come in. Or an agent that could use weather data and other information to decide when to water my garden and \n",
              "how long to water it for. Or an agent that could respond to people reporting bugs and automatically gather missing \n",
              "information and seek clarification on confusing parts of a bug report, and then route the bug to the appropriate \n",
              "team to resolve. Or an agent could use a camera to look at a Rubik's cube and then use robotic manipulators to \n",
              "solve it. Do I get any fun ones yet? Oh, here we go. I could use an agent also to build a travel planning agent \n",
              "that takes my preferences and needs into account, researches things like destinations and hotels, and even \n",
              "activities, and then suggests an itinerary for me, and use a collaborating agent to book everything all in one \n",
              "shot. Or I could build an agent that suggests an outfit for every day based on the weather and what's currently \n",
              "clean in my closet. All right, I think we're getting some pretty cool examples here for folks to work with. By the \n",
              "way, if you're looking for code for some of the agents that we've actually discussed here, these are available from\n",
              "Google, and we've put the links to that code in the description below. I think we've seen that there are lots of \n",
              "different types of agents, and it's more important to think about things being agentic than it is a specific \n",
              "definition about an agent or how to build it. But since we want to talk about building specifically, what are some \n",
              "things I should consider when I'm building an agent? So I think one of the most important things to remember is \n",
              "that an agent doesn't necessarily need to even incorporate an LLM. If your agent doesn't need generative AI \n",
              "capabilities, you probably don't need an LLM. You may be able to use a simple hard-coded algorithm to achieve your \n",
              "goal. That actually makes sense. Agents were around before LLMs took off and either used hard-coded rules or used \n",
              "custom-trained models things like BERT. I remember doing this as a part of robotic process automation bots even \n",
              "just a few years ago. And those options, and more importantly the use cases for those agents, are even more valid \n",
              "today than they were five years ago. Exactly. And if you do use an LLM, you can use tools like function calling to \n",
              "assist your agent in completing a task. You can also give your agent the ability to retrieve data from data \n",
              "storage, so you can use techniques that we've talked about already. Alright, so we've been talking about agents as \n",
              "if they're single entities. But I think agents can work together, right? One way we can think about agents is that \n",
              "we could have different agents with different functionalities or different tools, and the agents can even interact \n",
              "with each other to accomplish a goal. For example, maybe I would have a shipping agent and a customer service agent\n",
              "that can interact to figure out why a package never arrived for a customer. You could have these agents working \n",
              "together also to improve the output. Maybe you have one agent that produces blog posts and a second agent that \n",
              "critiques them for style and technical accuracy. You can create a loop where the critical agent feedback goes to \n",
              "the producer agent, and they go back and forth refining the blog post until the critic agent doesn't find any more \n",
              "issues. So if I wanted to get started building agents, is there a best place to begin? It does really depend on \n",
              "your particular agent. A very simple agent could just be some business logic with an interface to enable \n",
              "interaction with the environment or with the user. And since we're talking about degrees of agentic behavior, some \n",
              "prompts and business logic packaged together could also be an agent. There are some frameworks for helping you \n",
              "build agents, and if you want some guidance about this, they have more things around architecture and orchestration\n",
              "and even build. Also, general purpose frameworks for interacting with models can be used to build agents, \n",
              "especially agents that are designed around a goal and some tools to achieve that specific goal. So if you'd like to\n",
              "try building an agent yourself, we've included some links to tutorials using different approaches and different \n",
              "degrees of agenticness. And we also have a tutorial for building agents and getting started with Vertex AI Agent \n",
              "Builder. And there's even one more tutorial on building a vacation planning agent using Firebase and Genkit. Lots \n",
              "of options. Can't wait to see what you build. Happy prompting, everyone.\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"> Hey Jason, what's today's topic? Agents. Good one. So I hear a lot of folks talking about agents and agentic \n",
              "architectures, but let's back up a bit. What's an agent? Well, that's the fun part. No one seems to agree what \n",
              "exactly an agent is. So in my mental model, an agent is an AI with a job. and the tools necessary to do that job. \n",
              "I've referred to them before as like AI microservices or tiny little AI apps. How do you think about them? You mean\n",
              "something like a customer service agent? I always try to think of things in this like I would something in the real\n",
              "world. An agent should be something that helps me to get something done more effectively, get it done better, or to\n",
              "accomplish something I wouldn't have been able to do otherwise, like a customer service agent helping me. So some \n",
              "folks say that agents must act autonomously, but acting autonomous is also kind of vague. If an agent uses tools to\n",
              "research a topic and then summarizes the results for you, is that acting autonomously? Other people say that agents\n",
              "must be able to reason about their environment based on the inputs and based on their own outputs. And I wrote in \n",
              "one place that an agent was a system that was prompted an LLM more than once before returning to the user. I think \n",
              "we can agree that the definitions vary wildly. So how can we talk about agents without having a clear idea about \n",
              "what they are. So Andrew Ng wrote a blog post about this challenge and suggested that instead of talking about \n",
              "things as agent versus not agent, we just acknowledge that different kinds of systems can be agentic to different \n",
              "degrees. Okay. So an AI completing a task autonomously is agentic, and an AI researching and summarizing for you is\n",
              "also agentic, but maybe not to the same kind of degree of agentic. Yes. So to better appreciate the full range of \n",
              "agenticness, let's do a quick speed round of all the different use cases for agents. Go first. All right. An agent \n",
              "that could help me review my emails and create tasks on a digital to-do list based on the highest priority emails \n",
              "that come in. Or an agent that could use weather data and other information to decide when to water my garden and \n",
              "how long to water it for. Or an agent that could respond to people reporting bugs and automatically gather missing \n",
              "information and seek clarification on confusing parts of a bug report, and then route the bug to the appropriate \n",
              "team to resolve. Or an agent could use a camera to look at a Rubik's cube and then use robotic manipulators to \n",
              "solve it. Do I get any fun ones yet? Oh, here we go. I could use an agent also to build a travel planning agent \n",
              "that takes my preferences and needs into account, researches things like destinations and hotels, and even \n",
              "activities, and then suggests an itinerary for me, and use a collaborating agent to book everything all in one \n",
              "shot. Or I could build an agent that suggests an outfit for every day based on the weather and what's currently \n",
              "clean in my closet. All right, I think we're getting some pretty cool examples here for folks to work with. By the \n",
              "way, if you're looking for code for some of the agents that we've actually discussed here, these are available from\n",
              "Google, and we've put the links to that code in the description below. I think we've seen that there are lots of \n",
              "different types of agents, and it's more important to think about things being agentic than it is a specific \n",
              "definition about an agent or how to build it. But since we want to talk about building specifically, what are some \n",
              "things I should consider when I'm building an agent? So I think one of the most important things to remember is \n",
              "that an agent doesn't necessarily need to even incorporate an LLM. If your agent doesn't need generative AI \n",
              "capabilities, you probably don't need an LLM. You may be able to use a simple hard-coded algorithm to achieve your \n",
              "goal. That actually makes sense. Agents were around before LLMs took off and either used hard-coded rules or used \n",
              "custom-trained models things like BERT. I remember doing this as a part of robotic process automation bots even \n",
              "just a few years ago. And those options, and more importantly the use cases for those agents, are even more valid \n",
              "today than they were five years ago. Exactly. And if you do use an LLM, you can use tools like function calling to \n",
              "assist your agent in completing a task. You can also give your agent the ability to retrieve data from data \n",
              "storage, so you can use techniques that we've talked about already. Alright, so we've been talking about agents as \n",
              "if they're single entities. But I think agents can work together, right? One way we can think about agents is that \n",
              "we could have different agents with different functionalities or different tools, and the agents can even interact \n",
              "with each other to accomplish a goal. For example, maybe I would have a shipping agent and a customer service agent\n",
              "that can interact to figure out why a package never arrived for a customer. You could have these agents working \n",
              "together also to improve the output. Maybe you have one agent that produces blog posts and a second agent that \n",
              "critiques them for style and technical accuracy. You can create a loop where the critical agent feedback goes to \n",
              "the producer agent, and they go back and forth refining the blog post until the critic agent doesn't find any more \n",
              "issues. So if I wanted to get started building agents, is there a best place to begin? It does really depend on \n",
              "your particular agent. A very simple agent could just be some business logic with an interface to enable \n",
              "interaction with the environment or with the user. And since we're talking about degrees of agentic behavior, some \n",
              "prompts and business logic packaged together could also be an agent. There are some frameworks for helping you \n",
              "build agents, and if you want some guidance about this, they have more things around architecture and orchestration\n",
              "and even build. Also, general purpose frameworks for interacting with models can be used to build agents, \n",
              "especially agents that are designed around a goal and some tools to achieve that specific goal. So if you'd like to\n",
              "try building an agent yourself, we've included some links to tutorials using different approaches and different \n",
              "degrees of agenticness. And we also have a tutorial for building agents and getting started with Vertex AI Agent \n",
              "Builder. And there's even one more tutorial on building a vacation planning agent using Firebase and Genkit. Lots \n",
              "of options. Can't wait to see what you build. Happy prompting, everyone.\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Chunking**"
      ],
      "metadata": {
        "id": "GlB23Ti9XXoc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain import hub\n",
        "from langchain_core.documents import Document\n",
        "from langchain_text_splitters import RecursiveCharacterTextSplitter"
      ],
      "metadata": {
        "id": "zSSCHb1bouHk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
        "all_splits = text_splitter.split_documents([Document(page_content=text)])\n",
        "console.print(all_splits)"
      ],
      "metadata": {
        "id": "a0P_UayuSUH5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "collapsed": true,
        "outputId": "ef8c73c3-f310-48e7-930c-9cc9d5f77599"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m[\u001b[0m\n",
              "    \u001b[1;35mDocument\u001b[0m\u001b[1m(\u001b[0m\n",
              "        \u001b[33mmetadata\u001b[0m=\u001b[1m{\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[33mpage_content\u001b[0m=\u001b[32m\"Hey\u001b[0m\u001b[32m Jason, what's today's topic? Agents. Good one. So I hear a lot of folks talking about \u001b[0m\n",
              "\u001b[32magents and agentic architectures, but let's back up a bit. What's an agent? Well, that's the fun part. No one seems\u001b[0m\n",
              "\u001b[32mto agree what exactly an agent is. So in my mental model, an agent is an AI with a job. and the tools necessary to \u001b[0m\n",
              "\u001b[32mdo that job. I've referred to them before as like AI microservices or tiny little AI apps. How do you think about \u001b[0m\n",
              "\u001b[32mthem? You mean something like a customer service agent? I always try to think of things in this like I would \u001b[0m\n",
              "\u001b[32msomething in the real world. An agent should be something that helps me to get something done more effectively, get\u001b[0m\n",
              "\u001b[32mit done better, or to accomplish something I wouldn't have been able to do otherwise, like a customer service agent\u001b[0m\n",
              "\u001b[32mhelping me. So some folks say that agents must act autonomously, but acting autonomous is also kind of vague. If an\u001b[0m\n",
              "\u001b[32magent uses tools to research a topic and then summarizes the results for you, is that acting autonomously?\"\u001b[0m\n",
              "    \u001b[1m)\u001b[0m,\n",
              "    \u001b[1;35mDocument\u001b[0m\u001b[1m(\u001b[0m\n",
              "        \u001b[33mmetadata\u001b[0m=\u001b[1m{\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[33mpage_content\u001b[0m=\u001b[32m'say that agents must act autonomously, but acting autonomous is also kind of vague. If an \u001b[0m\n",
              "\u001b[32magent uses tools to research a topic and then summarizes the results for you, is that acting autonomously? Other \u001b[0m\n",
              "\u001b[32mpeople say that agents must be able to reason about their environment based on the inputs and based on their own \u001b[0m\n",
              "\u001b[32moutputs. And I wrote in one place that an agent was a system that was prompted an LLM more than once before \u001b[0m\n",
              "\u001b[32mreturning to the user. I think we can agree that the definitions vary wildly. So how can we talk about agents \u001b[0m\n",
              "\u001b[32mwithout having a clear idea about what they are. So Andrew Ng wrote a blog post about this challenge and suggested \u001b[0m\n",
              "\u001b[32mthat instead of talking about things as agent versus not agent, we just acknowledge that different kinds of systems\u001b[0m\n",
              "\u001b[32mcan be agentic to different degrees. Okay. So an AI completing a task autonomously is agentic, and an AI \u001b[0m\n",
              "\u001b[32mresearching and summarizing for you is also agentic, but maybe not to the same kind of degree of agentic. Yes. So \u001b[0m\n",
              "\u001b[32mto better'\u001b[0m\n",
              "    \u001b[1m)\u001b[0m,\n",
              "    \u001b[1;35mDocument\u001b[0m\u001b[1m(\u001b[0m\n",
              "        \u001b[33mmetadata\u001b[0m=\u001b[1m{\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[33mpage_content\u001b[0m=\u001b[32m\"Okay\u001b[0m\u001b[32m. So an AI completing a task autonomously is agentic, and an AI researching and \u001b[0m\n",
              "\u001b[32msummarizing for you is also agentic, but maybe not to the same kind of degree of agentic. Yes. So to better \u001b[0m\n",
              "\u001b[32mappreciate the full range of agenticness, let's do a quick speed round of all the different use cases for agents. \u001b[0m\n",
              "\u001b[32mGo first. All right. An agent that could help me review my emails and create tasks on a digital to-do list based on\u001b[0m\n",
              "\u001b[32mthe highest priority emails that come in. Or an agent that could use weather data and other information to decide \u001b[0m\n",
              "\u001b[32mwhen to water my garden and how long to water it for. Or an agent that could respond to people reporting bugs and \u001b[0m\n",
              "\u001b[32mautomatically gather missing information and seek clarification on confusing parts of a bug report, and then route \u001b[0m\n",
              "\u001b[32mthe bug to the appropriate team to resolve. Or an agent could use a camera to look at a Rubik's cube and then use \u001b[0m\n",
              "\u001b[32mrobotic manipulators to solve it. Do I get any fun ones yet? Oh, here we go. I could use an agent also to build a \u001b[0m\n",
              "\u001b[32mtravel\"\u001b[0m\n",
              "    \u001b[1m)\u001b[0m,\n",
              "    \u001b[1;35mDocument\u001b[0m\u001b[1m(\u001b[0m\n",
              "        \u001b[33mmetadata\u001b[0m=\u001b[1m{\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[33mpage_content\u001b[0m=\u001b[32m\"resolve\u001b[0m\u001b[32m. Or an agent could use a camera to look at a Rubik's cube and then use robotic \u001b[0m\n",
              "\u001b[32mmanipulators to solve it. Do I get any fun ones yet? Oh, here we go. I could use an agent also to build a travel \u001b[0m\n",
              "\u001b[32mplanning agent that takes my preferences and needs into account, researches things like destinations and hotels, \u001b[0m\n",
              "\u001b[32mand even activities, and then suggests an itinerary for me, and use a collaborating agent to book everything all in\u001b[0m\n",
              "\u001b[32mone shot. Or I could build an agent that suggests an outfit for every day based on the weather and what's currently\u001b[0m\n",
              "\u001b[32mclean in my closet. All right, I think we're getting some pretty cool examples here for folks to work with. By the \u001b[0m\n",
              "\u001b[32mway, if you're looking for code for some of the agents that we've actually discussed here, these are available from\u001b[0m\n",
              "\u001b[32mGoogle, and we've put the links to that code in the description below. I think we've seen that there are lots of \u001b[0m\n",
              "\u001b[32mdifferent types of agents, and it's more important to think about things being agentic than it is a specific\"\u001b[0m\n",
              "    \u001b[1m)\u001b[0m,\n",
              "    \u001b[1;35mDocument\u001b[0m\u001b[1m(\u001b[0m\n",
              "        \u001b[33mmetadata\u001b[0m=\u001b[1m{\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[33mpage_content\u001b[0m=\u001b[32m\"the\u001b[0m\u001b[32m links to that code in the description below. I think we've seen that there are lots of \u001b[0m\n",
              "\u001b[32mdifferent types of agents, and it's more important to think about things being agentic than it is a specific \u001b[0m\n",
              "\u001b[32mdefinition about an agent or how to build it. But since we want to talk about building specifically, what are some \u001b[0m\n",
              "\u001b[32mthings I should consider when I'm building an agent? So I think one of the most important things to remember is \u001b[0m\n",
              "\u001b[32mthat an agent doesn't necessarily need to even incorporate an LLM. If your agent doesn't need generative AI \u001b[0m\n",
              "\u001b[32mcapabilities, you probably don't need an LLM. You may be able to use a simple hard-coded algorithm to achieve your \u001b[0m\n",
              "\u001b[32mgoal. That actually makes sense. Agents were around before LLMs took off and either used hard-coded rules or used \u001b[0m\n",
              "\u001b[32mcustom-trained models things like BERT. I remember doing this as a part of robotic process automation bots even \u001b[0m\n",
              "\u001b[32mjust a few years ago. And those options, and more importantly the use cases for those agents, are even more valid \u001b[0m\n",
              "\u001b[32mtoday\"\u001b[0m\n",
              "    \u001b[1m)\u001b[0m,\n",
              "    \u001b[1;35mDocument\u001b[0m\u001b[1m(\u001b[0m\n",
              "        \u001b[33mmetadata\u001b[0m=\u001b[1m{\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[33mpage_content\u001b[0m=\u001b[32m\"BERT\u001b[0m\u001b[32m. I remember doing this as a part of robotic process automation bots even just a few \u001b[0m\n",
              "\u001b[32myears ago. And those options, and more importantly the use cases for those agents, are even more valid today than \u001b[0m\n",
              "\u001b[32mthey were five years ago. Exactly. And if you do use an LLM, you can use tools like function calling to assist your\u001b[0m\n",
              "\u001b[32magent in completing a task. You can also give your agent the ability to retrieve data from data storage, so you can\u001b[0m\n",
              "\u001b[32muse techniques that we've talked about already. Alright, so we've been talking about agents as if they're single \u001b[0m\n",
              "\u001b[32mentities. But I think agents can work together, right? One way we can think about agents is that we could have \u001b[0m\n",
              "\u001b[32mdifferent agents with different functionalities or different tools, and the agents can even interact with each \u001b[0m\n",
              "\u001b[32mother to accomplish a goal. For example, maybe I would have a shipping agent and a customer service agent that can \u001b[0m\n",
              "\u001b[32minteract to figure out why a package never arrived for a customer. You could have these agents working together\"\u001b[0m\n",
              "    \u001b[1m)\u001b[0m,\n",
              "    \u001b[1;35mDocument\u001b[0m\u001b[1m(\u001b[0m\n",
              "        \u001b[33mmetadata\u001b[0m=\u001b[1m{\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[33mpage_content\u001b[0m=\u001b[32m\"For\u001b[0m\u001b[32m example, maybe I would have a shipping agent and a customer service agent that can \u001b[0m\n",
              "\u001b[32minteract to figure out why a package never arrived for a customer. You could have these agents working together \u001b[0m\n",
              "\u001b[32malso to improve the output. Maybe you have one agent that produces blog posts and a second agent that critiques \u001b[0m\n",
              "\u001b[32mthem for style and technical accuracy. You can create a loop where the critical agent feedback goes to the producer\u001b[0m\n",
              "\u001b[32magent, and they go back and forth refining the blog post until the critic agent doesn't find any more issues. So if\u001b[0m\n",
              "\u001b[32mI wanted to get started building agents, is there a best place to begin? It does really depend on your particular \u001b[0m\n",
              "\u001b[32magent. A very simple agent could just be some business logic with an interface to enable interaction with the \u001b[0m\n",
              "\u001b[32menvironment or with the user. And since we're talking about degrees of agentic behavior, some prompts and business \u001b[0m\n",
              "\u001b[32mlogic packaged together could also be an agent. There are some frameworks for helping you build agents, and if you\"\u001b[0m\n",
              "    \u001b[1m)\u001b[0m,\n",
              "    \u001b[1;35mDocument\u001b[0m\u001b[1m(\u001b[0m\n",
              "        \u001b[33mmetadata\u001b[0m=\u001b[1m{\u001b[0m\u001b[1m}\u001b[0m,\n",
              "        \u001b[33mpage_content\u001b[0m=\u001b[32m\"And\u001b[0m\u001b[32m since we're talking about degrees of agentic behavior, some prompts and business logic \u001b[0m\n",
              "\u001b[32mpackaged together could also be an agent. There are some frameworks for helping you build agents, and if you want \u001b[0m\n",
              "\u001b[32msome guidance about this, they have more things around architecture and orchestration and even build. Also, general\u001b[0m\n",
              "\u001b[32mpurpose frameworks for interacting with models can be used to build agents, especially agents that are designed \u001b[0m\n",
              "\u001b[32maround a goal and some tools to achieve that specific goal. So if you'd like to try building an agent yourself, \u001b[0m\n",
              "\u001b[32mwe've included some links to tutorials using different approaches and different degrees of agenticness. And we also\u001b[0m\n",
              "\u001b[32mhave a tutorial for building agents and getting started with Vertex AI Agent Builder. And there's even one more \u001b[0m\n",
              "\u001b[32mtutorial on building a vacation planning agent using Firebase and Genkit. Lots of options. Can't wait to see what \u001b[0m\n",
              "\u001b[32myou build. Happy prompting, everyone.\"\u001b[0m\n",
              "    \u001b[1m)\u001b[0m\n",
              "\u001b[1m]\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">[</span>\n",
              "    <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Document</span><span style=\"font-weight: bold\">(</span>\n",
              "        <span style=\"color: #808000; text-decoration-color: #808000\">metadata</span>=<span style=\"font-weight: bold\">{}</span>,\n",
              "        <span style=\"color: #808000; text-decoration-color: #808000\">page_content</span>=<span style=\"color: #008000; text-decoration-color: #008000\">\"Hey Jason, what's today's topic? Agents. Good one. So I hear a lot of folks talking about </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">agents and agentic architectures, but let's back up a bit. What's an agent? Well, that's the fun part. No one seems</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">to agree what exactly an agent is. So in my mental model, an agent is an AI with a job. and the tools necessary to </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">do that job. I've referred to them before as like AI microservices or tiny little AI apps. How do you think about </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">them? You mean something like a customer service agent? I always try to think of things in this like I would </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">something in the real world. An agent should be something that helps me to get something done more effectively, get</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">it done better, or to accomplish something I wouldn't have been able to do otherwise, like a customer service agent</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">helping me. So some folks say that agents must act autonomously, but acting autonomous is also kind of vague. If an</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">agent uses tools to research a topic and then summarizes the results for you, is that acting autonomously?\"</span>\n",
              "    <span style=\"font-weight: bold\">)</span>,\n",
              "    <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Document</span><span style=\"font-weight: bold\">(</span>\n",
              "        <span style=\"color: #808000; text-decoration-color: #808000\">metadata</span>=<span style=\"font-weight: bold\">{}</span>,\n",
              "        <span style=\"color: #808000; text-decoration-color: #808000\">page_content</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'say that agents must act autonomously, but acting autonomous is also kind of vague. If an </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">agent uses tools to research a topic and then summarizes the results for you, is that acting autonomously? Other </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">people say that agents must be able to reason about their environment based on the inputs and based on their own </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">outputs. And I wrote in one place that an agent was a system that was prompted an LLM more than once before </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">returning to the user. I think we can agree that the definitions vary wildly. So how can we talk about agents </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">without having a clear idea about what they are. So Andrew Ng wrote a blog post about this challenge and suggested </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">that instead of talking about things as agent versus not agent, we just acknowledge that different kinds of systems</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">can be agentic to different degrees. Okay. So an AI completing a task autonomously is agentic, and an AI </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">researching and summarizing for you is also agentic, but maybe not to the same kind of degree of agentic. Yes. So </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">to better'</span>\n",
              "    <span style=\"font-weight: bold\">)</span>,\n",
              "    <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Document</span><span style=\"font-weight: bold\">(</span>\n",
              "        <span style=\"color: #808000; text-decoration-color: #808000\">metadata</span>=<span style=\"font-weight: bold\">{}</span>,\n",
              "        <span style=\"color: #808000; text-decoration-color: #808000\">page_content</span>=<span style=\"color: #008000; text-decoration-color: #008000\">\"Okay. So an AI completing a task autonomously is agentic, and an AI researching and </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">summarizing for you is also agentic, but maybe not to the same kind of degree of agentic. Yes. So to better </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">appreciate the full range of agenticness, let's do a quick speed round of all the different use cases for agents. </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">Go first. All right. An agent that could help me review my emails and create tasks on a digital to-do list based on</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">the highest priority emails that come in. Or an agent that could use weather data and other information to decide </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">when to water my garden and how long to water it for. Or an agent that could respond to people reporting bugs and </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">automatically gather missing information and seek clarification on confusing parts of a bug report, and then route </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">the bug to the appropriate team to resolve. Or an agent could use a camera to look at a Rubik's cube and then use </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">robotic manipulators to solve it. Do I get any fun ones yet? Oh, here we go. I could use an agent also to build a </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">travel\"</span>\n",
              "    <span style=\"font-weight: bold\">)</span>,\n",
              "    <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Document</span><span style=\"font-weight: bold\">(</span>\n",
              "        <span style=\"color: #808000; text-decoration-color: #808000\">metadata</span>=<span style=\"font-weight: bold\">{}</span>,\n",
              "        <span style=\"color: #808000; text-decoration-color: #808000\">page_content</span>=<span style=\"color: #008000; text-decoration-color: #008000\">\"resolve. Or an agent could use a camera to look at a Rubik's cube and then use robotic </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">manipulators to solve it. Do I get any fun ones yet? Oh, here we go. I could use an agent also to build a travel </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">planning agent that takes my preferences and needs into account, researches things like destinations and hotels, </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">and even activities, and then suggests an itinerary for me, and use a collaborating agent to book everything all in</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">one shot. Or I could build an agent that suggests an outfit for every day based on the weather and what's currently</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">clean in my closet. All right, I think we're getting some pretty cool examples here for folks to work with. By the </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">way, if you're looking for code for some of the agents that we've actually discussed here, these are available from</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">Google, and we've put the links to that code in the description below. I think we've seen that there are lots of </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">different types of agents, and it's more important to think about things being agentic than it is a specific\"</span>\n",
              "    <span style=\"font-weight: bold\">)</span>,\n",
              "    <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Document</span><span style=\"font-weight: bold\">(</span>\n",
              "        <span style=\"color: #808000; text-decoration-color: #808000\">metadata</span>=<span style=\"font-weight: bold\">{}</span>,\n",
              "        <span style=\"color: #808000; text-decoration-color: #808000\">page_content</span>=<span style=\"color: #008000; text-decoration-color: #008000\">\"the links to that code in the description below. I think we've seen that there are lots of </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">different types of agents, and it's more important to think about things being agentic than it is a specific </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">definition about an agent or how to build it. But since we want to talk about building specifically, what are some </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">things I should consider when I'm building an agent? So I think one of the most important things to remember is </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">that an agent doesn't necessarily need to even incorporate an LLM. If your agent doesn't need generative AI </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">capabilities, you probably don't need an LLM. You may be able to use a simple hard-coded algorithm to achieve your </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">goal. That actually makes sense. Agents were around before LLMs took off and either used hard-coded rules or used </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">custom-trained models things like BERT. I remember doing this as a part of robotic process automation bots even </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">just a few years ago. And those options, and more importantly the use cases for those agents, are even more valid </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">today\"</span>\n",
              "    <span style=\"font-weight: bold\">)</span>,\n",
              "    <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Document</span><span style=\"font-weight: bold\">(</span>\n",
              "        <span style=\"color: #808000; text-decoration-color: #808000\">metadata</span>=<span style=\"font-weight: bold\">{}</span>,\n",
              "        <span style=\"color: #808000; text-decoration-color: #808000\">page_content</span>=<span style=\"color: #008000; text-decoration-color: #008000\">\"BERT. I remember doing this as a part of robotic process automation bots even just a few </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">years ago. And those options, and more importantly the use cases for those agents, are even more valid today than </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">they were five years ago. Exactly. And if you do use an LLM, you can use tools like function calling to assist your</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">agent in completing a task. You can also give your agent the ability to retrieve data from data storage, so you can</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">use techniques that we've talked about already. Alright, so we've been talking about agents as if they're single </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">entities. But I think agents can work together, right? One way we can think about agents is that we could have </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">different agents with different functionalities or different tools, and the agents can even interact with each </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">other to accomplish a goal. For example, maybe I would have a shipping agent and a customer service agent that can </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">interact to figure out why a package never arrived for a customer. You could have these agents working together\"</span>\n",
              "    <span style=\"font-weight: bold\">)</span>,\n",
              "    <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Document</span><span style=\"font-weight: bold\">(</span>\n",
              "        <span style=\"color: #808000; text-decoration-color: #808000\">metadata</span>=<span style=\"font-weight: bold\">{}</span>,\n",
              "        <span style=\"color: #808000; text-decoration-color: #808000\">page_content</span>=<span style=\"color: #008000; text-decoration-color: #008000\">\"For example, maybe I would have a shipping agent and a customer service agent that can </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">interact to figure out why a package never arrived for a customer. You could have these agents working together </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">also to improve the output. Maybe you have one agent that produces blog posts and a second agent that critiques </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">them for style and technical accuracy. You can create a loop where the critical agent feedback goes to the producer</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">agent, and they go back and forth refining the blog post until the critic agent doesn't find any more issues. So if</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">I wanted to get started building agents, is there a best place to begin? It does really depend on your particular </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">agent. A very simple agent could just be some business logic with an interface to enable interaction with the </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">environment or with the user. And since we're talking about degrees of agentic behavior, some prompts and business </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">logic packaged together could also be an agent. There are some frameworks for helping you build agents, and if you\"</span>\n",
              "    <span style=\"font-weight: bold\">)</span>,\n",
              "    <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Document</span><span style=\"font-weight: bold\">(</span>\n",
              "        <span style=\"color: #808000; text-decoration-color: #808000\">metadata</span>=<span style=\"font-weight: bold\">{}</span>,\n",
              "        <span style=\"color: #808000; text-decoration-color: #808000\">page_content</span>=<span style=\"color: #008000; text-decoration-color: #008000\">\"And since we're talking about degrees of agentic behavior, some prompts and business logic </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">packaged together could also be an agent. There are some frameworks for helping you build agents, and if you want </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">some guidance about this, they have more things around architecture and orchestration and even build. Also, general</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">purpose frameworks for interacting with models can be used to build agents, especially agents that are designed </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">around a goal and some tools to achieve that specific goal. So if you'd like to try building an agent yourself, </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">we've included some links to tutorials using different approaches and different degrees of agenticness. And we also</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">have a tutorial for building agents and getting started with Vertex AI Agent Builder. And there's even one more </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">tutorial on building a vacation planning agent using Firebase and Genkit. Lots of options. Can't wait to see what </span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">you build. Happy prompting, everyone.\"</span>\n",
              "    <span style=\"font-weight: bold\">)</span>\n",
              "<span style=\"font-weight: bold\">]</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Index**"
      ],
      "metadata": {
        "id": "vXbHP8qzXcxf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.vectorstores import InMemoryVectorStore\n",
        "from langchain_google_vertexai import VertexAIEmbeddings\n",
        "import vertexai\n",
        "from google.colab import userdata\n",
        "\n",
        "PROJECT_ID = userdata.get('PROJECT_ID')\n",
        "LOCATION = \"us-central1\"\n",
        "\n",
        "vertexai.init(project=PROJECT_ID, location=LOCATION)\n",
        "embeddings = VertexAIEmbeddings(model=\"text-embedding-004\")\n",
        "vector_store = InMemoryVectorStore(embeddings)"
      ],
      "metadata": {
        "id": "MXaNVgejJjjG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vector_store.add_documents(documents=all_splits)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y5HiAVfFJpag",
        "outputId": "0250f8c4-0e16-47fd-c257-313071ea1bda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['5537556c-ccad-48a5-b7ae-15804bf38f6b',\n",
              " '5cef1905-b878-429f-8c89-1655daab96f8',\n",
              " '5b60359f-6f5a-45da-a5ea-83e2732c7c82',\n",
              " '2c19762a-fcfd-43db-9f41-45998977d662',\n",
              " '9b60f5ad-bd5a-42d4-ae16-cebd97f1e08a',\n",
              " '162a6707-bfc8-4abf-8f8f-61053bb89a89',\n",
              " '4f85aa0e-f3dc-4d27-9abd-3b3e45335fa9',\n",
              " '881b34bf-e465-43dd-a88a-d81e54884140']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Getting DeepSeek from HuggingFace**"
      ],
      "metadata": {
        "id": "lK0mkRfpXj_H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_huggingface.llms import HuggingFacePipeline\n",
        "\n",
        "llm = HuggingFacePipeline.from_model_id(\n",
        "    model_id=\"deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B\",\n",
        "    task=\"text-generation\",\n",
        "    pipeline_kwargs={\"max_new_tokens\": 3000, \"temperature\":0.7},\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194,
          "referenced_widgets": [
            "8d141e3087374674a4e559f603f2c3d0",
            "bce4a0939f054479a0a1e36e9b95d0b4",
            "a8d18f63e04c49b6b104634af0c0d384",
            "35cd2a087d674d629585ba141b10a010",
            "608dfdd5c5ea4344a3868bf4436c4cd3",
            "5f9fe824589e41d59dc90de047a46d89",
            "1d8fed6207ea43edbe5203828176fd06",
            "5db980f9d9ef4f42a0e73bb64e4ac8fb",
            "e271b90654c543e593a38d0e7786785c",
            "b264960dff344f679ade6102ab3f445b",
            "37fb3be92db441cbb17190630c4a7688",
            "198e630f28d14ab4ba9019b6c797fff3",
            "1e705f3260774c36ba42035a9ab813d5",
            "d0e048a28c8f4188ae1f38936b00df5f",
            "42de39bb68894e52a62a80ac40860d54",
            "c475243aabbc4b7891b84ceddd26e7fa",
            "d27c5a45fcd5479c8d5f294e60b47530",
            "4e6d1a54485d4202a08cc827482db0a6",
            "bad1835aef574c619d61f89428c66d5c",
            "341e89284a3341a4b1d8582d857fca6b",
            "89385523c57e4cfda56242e1972f466e",
            "3215ab08151e46ccaa2d73d3dedb8ce9",
            "48f337701d5742559c43a831d60e012b",
            "e170f64209eb4f13862c3be912626057",
            "b86447609b624b5ab8b9d0f52017bcee",
            "f674d5ecbe90410d955989c7fa68c41b",
            "451a00d6e50e46d1b6cec1a7e8d69821",
            "4a55220ab45f4230823d1184fe9ca3c4",
            "ef3080f94eb2489ba2e2e49a5290aa61",
            "7be879343c7d48eebb21636604b9fff6",
            "48c561f8ec2b4430a253726cd579e1f8",
            "49d18b4a704c487592cb70c5b7b06170",
            "d58fbd4dd2e4413da099cc1682308715",
            "d344c3d5b31f4935b873b0ab2bc08c4a",
            "4d7da6dae87947deaa4871efd9ad5fd7",
            "12a304d93ab44e738b3375253c5f1752",
            "56ede499bf9348cbac2407b952121645",
            "ee76eaa4672e4ec2862ef06ba5e88f94",
            "922128d9478842ad811b47c4268a3e1f",
            "4279749883144419a2baef5a1b81c6d6",
            "076f99e7deb04579a40e9f1694cc43ff",
            "662c4c77fd1849e49ba7b13bd958c295",
            "69d5f68e66e84cbe8a4c92abd4986312",
            "b3dda04fb18c45d8a6b9b852a5ee6c63",
            "8ea41800c6154c8a8fca82a119147334",
            "56bd66b9279d4090b22559a326548b8f",
            "5f4916aeb2e441b0960b06fefb75fb2d",
            "a33da8d325cb4433af853bd16c5454d2",
            "c32dd20ae9bd4c6c84d24d4f065cf319",
            "eb66fcd18ed24b66bbba39e9f06b5a72",
            "d3b10247cd2d4320bd71db599ca47691",
            "70ce7673ef204786a656b38958209e8d",
            "84d9caf516c448ee8566c935f23c8b60",
            "0a415db460424707800a0a2fa5677a55",
            "bc8b11832f004c829fd6f6850718cd1c"
          ]
        },
        "id": "SM5iOotxTFDo",
        "outputId": "b0d0ee28-c677-4635-a807-ffc2a0f5a2f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/3.06k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8d141e3087374674a4e559f603f2c3d0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/7.03M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "198e630f28d14ab4ba9019b6c797fff3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/679 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "48f337701d5742559c43a831d60e012b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/3.55G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d344c3d5b31f4935b873b0ab2bc08c4a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/181 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8ea41800c6154c8a8fca82a119147334"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Device set to use cuda:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Retrieve and Generation**"
      ],
      "metadata": {
        "id": "YffRrl_YYOlz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = hub.pull(\"rlm/rag-prompt\")\n",
        "\n",
        "question = \"What's an agent?\"\n",
        "retrieved_docs = vector_store.similarity_search(question)\n",
        "docs_content = \"\\n\\n\".join(doc.page_content for doc in retrieved_docs)\n",
        "chain = prompt | llm\n",
        "answer = chain.invoke({\"question\": question, \"context\": docs_content})\n",
        "console.print(answer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "VX7tj-BEJund",
        "outputId": "ce839eb7-9cc7-4771-fe51-bb043156bfcf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/langsmith/client.py:253: LangSmithMissingAPIKeyWarning: API key must be provided when using hosted LangSmith API\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-13dbd71cbacd>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdocs_content\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"\\n\\n\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpage_content\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdoc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mretrieved_docs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mchain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprompt\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mllm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0manswer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minvoke\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"question\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mquestion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"context\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdocs_content\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mconsole\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manswer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain_core/runnables/base.py\u001b[0m in \u001b[0;36minvoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   3014\u001b[0m                     \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minvoke\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3015\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3016\u001b[0;31m                     \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minvoke\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3017\u001b[0m         \u001b[0;31m# finish the root run\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3018\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain_core/language_models/llms.py\u001b[0m in \u001b[0;36minvoke\u001b[0;34m(self, input, config, stop, **kwargs)\u001b[0m\n\u001b[1;32m    385\u001b[0m         \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensure_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m         return (\n\u001b[0;32m--> 387\u001b[0;31m             self.generate_prompt(\n\u001b[0m\u001b[1;32m    388\u001b[0m                 \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    389\u001b[0m                 \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain_core/language_models/llms.py\u001b[0m in \u001b[0;36mgenerate_prompt\u001b[0;34m(self, prompts, stop, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    758\u001b[0m     ) -> LLMResult:\n\u001b[1;32m    759\u001b[0m         \u001b[0mprompt_strings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprompts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 760\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprompt_strings\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    761\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    762\u001b[0m     async def agenerate_prompt(\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain_core/language_models/llms.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, prompts, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[1;32m    961\u001b[0m                 )\n\u001b[1;32m    962\u001b[0m             ]\n\u001b[0;32m--> 963\u001b[0;31m             output = self._generate_helper(\n\u001b[0m\u001b[1;32m    964\u001b[0m                 \u001b[0mprompts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_managers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_arg_supported\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    965\u001b[0m             )\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain_core/language_models/llms.py\u001b[0m in \u001b[0;36m_generate_helper\u001b[0;34m(self, prompts, stop, run_managers, new_arg_supported, **kwargs)\u001b[0m\n\u001b[1;32m    782\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    783\u001b[0m             output = (\n\u001b[0;32m--> 784\u001b[0;31m                 self._generate(\n\u001b[0m\u001b[1;32m    785\u001b[0m                     \u001b[0mprompts\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    786\u001b[0m                     \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/langchain_huggingface/llms/huggingface_pipeline.py\u001b[0m in \u001b[0;36m_generate\u001b[0;34m(self, prompts, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    293\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m             \u001b[0;31m# Process batch of prompts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m             responses = self.pipeline(\n\u001b[0m\u001b[1;32m    296\u001b[0m                 \u001b[0mbatch_prompts\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m                 \u001b[0;34m**\u001b[0m\u001b[0mpipeline_kwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/pipelines/text_generation.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, text_inputs, **kwargs)\u001b[0m\n\u001b[1;32m    270\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    271\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    273\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m     def preprocess(\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/pipelines/base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, num_workers, batch_size, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1280\u001b[0m                     \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_workers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreprocess_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforward_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpostprocess_params\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1281\u001b[0m                 )\n\u001b[0;32m-> 1282\u001b[0;31m                 \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_iterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1283\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1284\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/pipelines/pt_utils.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m         \u001b[0;31m# We're out of items within a batch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m         \u001b[0mitem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    125\u001b[0m         \u001b[0mprocessed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m         \u001b[0;31m# We now have a batch of \"inferred things\".\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/pipelines/pt_utils.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    123\u001b[0m         \u001b[0;31m# We're out of items within a batch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m         \u001b[0mitem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m         \u001b[0mprocessed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    126\u001b[0m         \u001b[0;31m# We now have a batch of \"inferred things\".\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloader_batch_size\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/pipelines/base.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, model_inputs, **forward_params)\u001b[0m\n\u001b[1;32m   1206\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0minference_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1207\u001b[0m                     \u001b[0mmodel_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_tensor_on_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1208\u001b[0;31m                     \u001b[0mmodel_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mforward_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1209\u001b[0m                     \u001b[0mmodel_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_tensor_on_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_outputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cpu\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1210\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/pipelines/text_generation.py\u001b[0m in \u001b[0;36m_forward\u001b[0;34m(self, model_inputs, **generate_kwargs)\u001b[0m\n\u001b[1;32m    368\u001b[0m             \u001b[0mgenerate_kwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"generation_config\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeneration_config\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 370\u001b[0;31m         \u001b[0mgenerated_sequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mgenerate_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    371\u001b[0m         \u001b[0mout_b\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerated_sequence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    372\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"pt\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    114\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/generation/utils.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, inputs, generation_config, logits_processor, stopping_criteria, prefix_allowed_tokens_fn, synced_gpus, assistant_model, streamer, negative_prompt_ids, negative_prompt_attention_mask, **kwargs)\u001b[0m\n\u001b[1;32m   2250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2251\u001b[0m             \u001b[0;31m# 12. run sample (it degenerates to greedy search when `generation_config.do_sample=False`)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2252\u001b[0;31m             result = self._sample(\n\u001b[0m\u001b[1;32m   2253\u001b[0m                 \u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2254\u001b[0m                 \u001b[0mlogits_processor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprepared_logits_processor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/generation/utils.py\u001b[0m in \u001b[0;36m_sample\u001b[0;34m(self, input_ids, logits_processor, stopping_criteria, generation_config, synced_gpus, streamer, **model_kwargs)\u001b[0m\n\u001b[1;32m   3252\u001b[0m                 \u001b[0mis_prefill\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3253\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3254\u001b[0;31m                 \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mmodel_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3256\u001b[0m             \u001b[0;31m# synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1735\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1736\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1738\u001b[0m     \u001b[0;31m# torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1745\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1746\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1749\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/models/qwen2/modeling_qwen2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict, cache_position, num_logits_to_keep, **loss_kwargs)\u001b[0m\n\u001b[1;32m   1163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1164\u001b[0m         \u001b[0;31m# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1165\u001b[0;31m         outputs = self.model(\n\u001b[0m\u001b[1;32m   1166\u001b[0m             \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1167\u001b[0m             \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1735\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1736\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1738\u001b[0m     \u001b[0;31m# torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1745\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1746\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1749\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/models/qwen2/modeling_qwen2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict, cache_position)\u001b[0m\n\u001b[1;32m    893\u001b[0m                 )\n\u001b[1;32m    894\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m                 layer_outputs = decoder_layer(\n\u001b[0m\u001b[1;32m    896\u001b[0m                     \u001b[0mhidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m                     \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcausal_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1735\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1736\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1738\u001b[0m     \u001b[0;31m# torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1745\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1746\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1749\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/models/qwen2/modeling_qwen2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, position_ids, past_key_value, output_attentions, use_cache, cache_position, position_embeddings, **kwargs)\u001b[0m\n\u001b[1;32m    621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    622\u001b[0m         \u001b[0;31m# Self Attention\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 623\u001b[0;31m         hidden_states, self_attn_weights, present_key_value = self.self_attn(\n\u001b[0m\u001b[1;32m    624\u001b[0m             \u001b[0mhidden_states\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    625\u001b[0m             \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1735\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1736\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1738\u001b[0m     \u001b[0;31m# torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1745\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1746\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1749\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/models/qwen2/modeling_qwen2.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, position_ids, past_key_value, output_attentions, use_cache, cache_position, position_embeddings)\u001b[0m\n\u001b[1;32m    552\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m         \u001b[0mattn_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mattn_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontiguous\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 554\u001b[0;31m         \u001b[0mattn_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mattn_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbsz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mq_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhidden_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    555\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    556\u001b[0m         \u001b[0mattn_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mo_proj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattn_output\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Using LangGraph**"
      ],
      "metadata": {
        "id": "gZ7Qzq0cYSiR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langgraph"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9AiuGY1HfG7L",
        "outputId": "656d148a-f48c-4b0d-9fa2-693651b8b141"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langgraph\n",
            "  Downloading langgraph-0.2.69-py3-none-any.whl.metadata (17 kB)\n",
            "Requirement already satisfied: langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.2,!=0.3.20,!=0.3.21,!=0.3.22,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43 in /usr/local/lib/python3.11/dist-packages (from langgraph) (0.3.33)\n",
            "Collecting langgraph-checkpoint<3.0.0,>=2.0.10 (from langgraph)\n",
            "  Downloading langgraph_checkpoint-2.0.10-py3-none-any.whl.metadata (4.6 kB)\n",
            "Collecting langgraph-sdk<0.2.0,>=0.1.42 (from langgraph)\n",
            "  Downloading langgraph_sdk-0.1.51-py3-none-any.whl.metadata (1.8 kB)\n",
            "Requirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.11/dist-packages (from langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.2,!=0.3.20,!=0.3.21,!=0.3.22,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43->langgraph) (6.0.2)\n",
            "Requirement already satisfied: jsonpatch<2.0,>=1.33 in /usr/local/lib/python3.11/dist-packages (from langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.2,!=0.3.20,!=0.3.21,!=0.3.22,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43->langgraph) (1.33)\n",
            "Requirement already satisfied: langsmith<0.4,>=0.1.125 in /usr/local/lib/python3.11/dist-packages (from langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.2,!=0.3.20,!=0.3.21,!=0.3.22,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43->langgraph) (0.3.2)\n",
            "Requirement already satisfied: packaging<25,>=23.2 in /usr/local/lib/python3.11/dist-packages (from langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.2,!=0.3.20,!=0.3.21,!=0.3.22,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43->langgraph) (24.2)\n",
            "Requirement already satisfied: pydantic<3.0.0,>=2.5.2 in /usr/local/lib/python3.11/dist-packages (from langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.2,!=0.3.20,!=0.3.21,!=0.3.22,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43->langgraph) (2.10.6)\n",
            "Requirement already satisfied: tenacity!=8.4.0,<10.0.0,>=8.1.0 in /usr/local/lib/python3.11/dist-packages (from langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.2,!=0.3.20,!=0.3.21,!=0.3.22,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43->langgraph) (9.0.0)\n",
            "Requirement already satisfied: typing-extensions>=4.7 in /usr/local/lib/python3.11/dist-packages (from langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.2,!=0.3.20,!=0.3.21,!=0.3.22,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43->langgraph) (4.12.2)\n",
            "Requirement already satisfied: msgpack<2.0.0,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from langgraph-checkpoint<3.0.0,>=2.0.10->langgraph) (1.1.0)\n",
            "Requirement already satisfied: httpx>=0.25.2 in /usr/local/lib/python3.11/dist-packages (from langgraph-sdk<0.2.0,>=0.1.42->langgraph) (0.28.1)\n",
            "Requirement already satisfied: orjson>=3.10.1 in /usr/local/lib/python3.11/dist-packages (from langgraph-sdk<0.2.0,>=0.1.42->langgraph) (3.10.15)\n",
            "Requirement already satisfied: anyio in /usr/local/lib/python3.11/dist-packages (from httpx>=0.25.2->langgraph-sdk<0.2.0,>=0.1.42->langgraph) (3.7.1)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.11/dist-packages (from httpx>=0.25.2->langgraph-sdk<0.2.0,>=0.1.42->langgraph) (2024.12.14)\n",
            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.11/dist-packages (from httpx>=0.25.2->langgraph-sdk<0.2.0,>=0.1.42->langgraph) (1.0.7)\n",
            "Requirement already satisfied: idna in /usr/local/lib/python3.11/dist-packages (from httpx>=0.25.2->langgraph-sdk<0.2.0,>=0.1.42->langgraph) (3.10)\n",
            "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.11/dist-packages (from httpcore==1.*->httpx>=0.25.2->langgraph-sdk<0.2.0,>=0.1.42->langgraph) (0.14.0)\n",
            "Requirement already satisfied: jsonpointer>=1.9 in /usr/local/lib/python3.11/dist-packages (from jsonpatch<2.0,>=1.33->langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.2,!=0.3.20,!=0.3.21,!=0.3.22,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43->langgraph) (3.0.0)\n",
            "Requirement already satisfied: requests<3,>=2 in /usr/local/lib/python3.11/dist-packages (from langsmith<0.4,>=0.1.125->langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.2,!=0.3.20,!=0.3.21,!=0.3.22,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43->langgraph) (2.32.3)\n",
            "Requirement already satisfied: requests-toolbelt<2.0.0,>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from langsmith<0.4,>=0.1.125->langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.2,!=0.3.20,!=0.3.21,!=0.3.22,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43->langgraph) (1.0.0)\n",
            "Requirement already satisfied: zstandard<0.24.0,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from langsmith<0.4,>=0.1.125->langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.2,!=0.3.20,!=0.3.21,!=0.3.22,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43->langgraph) (0.23.0)\n",
            "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.11/dist-packages (from pydantic<3.0.0,>=2.5.2->langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.2,!=0.3.20,!=0.3.21,!=0.3.22,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43->langgraph) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.27.2 in /usr/local/lib/python3.11/dist-packages (from pydantic<3.0.0,>=2.5.2->langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.2,!=0.3.20,!=0.3.21,!=0.3.22,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43->langgraph) (2.27.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2->langsmith<0.4,>=0.1.125->langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.2,!=0.3.20,!=0.3.21,!=0.3.22,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43->langgraph) (3.4.1)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2->langsmith<0.4,>=0.1.125->langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.15,!=0.3.16,!=0.3.17,!=0.3.18,!=0.3.19,!=0.3.2,!=0.3.20,!=0.3.21,!=0.3.22,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43->langgraph) (2.3.0)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.11/dist-packages (from anyio->httpx>=0.25.2->langgraph-sdk<0.2.0,>=0.1.42->langgraph) (1.3.1)\n",
            "Downloading langgraph-0.2.69-py3-none-any.whl (148 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m148.7/148.7 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading langgraph_checkpoint-2.0.10-py3-none-any.whl (37 kB)\n",
            "Downloading langgraph_sdk-0.1.51-py3-none-any.whl (44 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.7/44.7 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: langgraph-sdk, langgraph-checkpoint, langgraph\n",
            "Successfully installed langgraph-0.2.69 langgraph-checkpoint-2.0.10 langgraph-sdk-0.1.51\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from typing_extensions import List, TypedDict\n",
        "class State(TypedDict):\n",
        "    question: str\n",
        "    context: List[Document]\n",
        "    answer: str"
      ],
      "metadata": {
        "id": "dYx5-sbNYVJK"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Define application steps**"
      ],
      "metadata": {
        "id": "c3M95KobYYaW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langgraph.graph import START, StateGraph\n",
        "\n",
        "def retrieve(state: State):\n",
        "    retrieved_docs = vector_store.similarity_search(state[\"question\"])\n",
        "    return {\"context\": retrieved_docs}"
      ],
      "metadata": {
        "id": "FjYNRsXGYbXY"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate(state: State):\n",
        "    prompt = hub.pull(\"rlm/rag-prompt\")\n",
        "    docs_content = \"\\n\\n\".join(doc.page_content for doc in state[\"context\"])\n",
        "    messages = prompt.invoke({\"question\": state[\"question\"], \"context\": docs_content})\n",
        "    response = llm.invoke(messages)\n",
        "    return {\"answer\": response}"
      ],
      "metadata": {
        "id": "oZVcDvTtYeU5"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "graph_builder = StateGraph(State).add_sequence([retrieve, generate])\n",
        "graph_builder.add_edge(START, \"retrieve\")\n",
        "graph = graph_builder.compile()"
      ],
      "metadata": {
        "id": "F29KOY5KYgk7"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response = graph.invoke({\"question\": \"What's an agent?\"})\n",
        "print(response[\"answer\"])"
      ],
      "metadata": {
        "id": "cbt4SFdsYjTj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb19c97c-0a3d-4b84-e27a-bda671fd61d4"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/langsmith/client.py:253: LangSmithMissingAPIKeyWarning: API key must be provided when using hosted LangSmith API\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Human: You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\n",
            "Question: What's an agent? \n",
            "Context: Hey Jason, what's today's topic? Agents. Good one. So I hear a lot of folks talking about agents and agentic architectures, but let's back up a bit. What's an agent? Well, that's the fun part. No one seems to agree what exactly an agent is. So in my mental model, an agent is an AI with a job. and the tools necessary to do that job. I've referred to them before as like AI microservices or tiny little AI apps. How do you think about them? You mean something like a customer service agent? I always try to think of things in this like I would something in the real world. An agent should be something that helps me to get something done more effectively, get it done better, or to accomplish something I wouldn't have been able to do otherwise, like a customer service agent helping me. So some folks say that agents must act autonomously, but acting autonomous is also kind of vague. If an agent uses tools to research a topic and then summarizes the results for you, is that acting autonomously?\n",
            "\n",
            "say that agents must act autonomously, but acting autonomous is also kind of vague. If an agent uses tools to research a topic and then summarizes the results for you, is that acting autonomously? Other people say that agents must be able to reason about their environment based on the inputs and based on their own outputs. And I wrote in one place that an agent was a system that was prompted an LLM more than once before returning to the user. I think we can agree that the definitions vary wildly. So how can we talk about agents without having a clear idea about what they are. So Andrew Ng wrote a blog post about this challenge and suggested that instead of talking about things as agent versus not agent, we just acknowledge that different kinds of systems can be agentic to different degrees. Okay. So an AI completing a task autonomously is agentic, and an AI researching and summarizing for you is also agentic, but maybe not to the same kind of degree of agentic. Yes. So to better\n",
            "\n",
            "the links to that code in the description below. I think we've seen that there are lots of different types of agents, and it's more important to think about things being agentic than it is a specific definition about an agent or how to build it. But since we want to talk about building specifically, what are some things I should consider when I'm building an agent? So I think one of the most important things to remember is that an agent doesn't necessarily need to even incorporate an LLM. If your agent doesn't need generative AI capabilities, you probably don't need an LLM. You may be able to use a simple hard-coded algorithm to achieve your goal. That actually makes sense. Agents were around before LLMs took off and either used hard-coded rules or used custom-trained models things like BERT. I remember doing this as a part of robotic process automation bots even just a few years ago. And those options, and more importantly the use cases for those agents, are even more valid today\n",
            "\n",
            "For example, maybe I would have a shipping agent and a customer service agent that can interact to figure out why a package never arrived for a customer. You could have these agents working together also to improve the output. Maybe you have one agent that produces blog posts and a second agent that critiques them for style and technical accuracy. You can create a loop where the critical agent feedback goes to the producer agent, and they go back and forth refining the blog post until the critic agent doesn't find any more issues. So if I wanted to get started building agents, is there a best place to begin? It does really depend on your particular agent. A very simple agent could just be some business logic with an interface to enable interaction with the environment or with the user. And since we're talking about degrees of agentic behavior, some prompts and business logic packaged together could also be an agent. There are some frameworks for helping you build agents, and if you \n",
            "Answer: An agent is an AI system designed to perform a specific task or function. They can be microservices, applications, or even entire systems. Agents can be autonomous, meaning they operate independently without external intervention, or agentic, where they assist humans by providing information, improving tasks, or solving problems. Agents can use various tools like LLMs, NLP, or custom models. Building an agent involves identifying the task, designing the AI system, defining its objectives, and ensuring it can operate effectively in different environments. Agents can be simple or complex, depending on the task and the AI's capabilities. The key is to design agents that are adaptable, capable of improving over time, and aligned with the goals of the user or organization. Frameworks and tools help in structuring the agent's architecture and training it to perform its tasks efficiently.\n",
            "</think>\n",
            "\n",
            "An agent is an AI system designed to perform a specific task or function. They can be microservices, applications, or entire systems. Agents can be autonomous or agentic, depending on whether they operate independently or assist humans. They can use tools like LLMs, NLP, or custom models. Building an agent involves designing a system to perform tasks, defining objectives, and ensuring it operates effectively in various environments. Frameworks and tools assist in structuring and training the agent.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import Image, display\n",
        "\n",
        "display(Image(graph.get_graph().draw_mermaid_png()))"
      ],
      "metadata": {
        "id": "5xlnbeZTYlLQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "b8f34555-5595-4ad8-f286-c700853ef718"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}