{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yc7rdoHKfTGU",
        "outputId": "d5eb9bfe-7e64-4f44-88f7-113291790cec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/67.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.3/67.3 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m36.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m611.1/611.1 kB\u001b[0m \u001b[31m25.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m56.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.4/44.4 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m284.2/284.2 kB\u001b[0m \u001b[31m17.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m95.2/95.2 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m57.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.9/50.9 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m101.6/101.6 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.0/16.0 MB\u001b[0m \u001b[31m61.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.9/55.9 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.0/92.0 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m98.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m80.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m46.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m87.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.5/62.5 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m459.8/459.8 kB\u001b[0m \u001b[31m34.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.0/72.0 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.0/4.0 MB\u001b[0m \u001b[31m94.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m454.8/454.8 kB\u001b[0m \u001b[31m32.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "%pip install --upgrade --quiet langchain_community langchain langchain-core langchain_huggingface langchain-chroma"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "B99BYYo6bHKj"
      },
      "outputs": [],
      "source": [
        "%pip install --upgrade --quiet langchain_google_genai langgraph"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "0h2yNOuOcy-E"
      },
      "outputs": [],
      "source": [
        "# Load metadata.jsonl\n",
        "import json\n",
        "\n",
        "# Load the metadata.jsonl file\n",
        "with open('/content/metadata.jsonl', 'r') as jsonl_file:\n",
        "    json_list = list(jsonl_file)\n",
        "\n",
        "json_QA = []\n",
        "for json_str in json_list:\n",
        "    json_data = json.loads(json_str)\n",
        "    json_QA.append(json_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2gBdvVhsdpCN",
        "outputId": "16e3f0a5-a7b7-4e2a-d87e-23d83c117a3c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'task_id': 'c61d22de-5f6c-4958-a7f6-5e9707bd3466',\n",
              " 'Question': 'A paper about AI regulation that was originally submitted to arXiv.org in June 2022 shows a figure with three axes, where each axis has a label word at both ends. Which of these words is used to describe a type of society in a Physics and Society article submitted to arXiv.org on August 11, 2016?',\n",
              " 'Level': 2,\n",
              " 'Final answer': 'egalitarian',\n",
              " 'file_name': '',\n",
              " 'Annotator Metadata': {'Steps': '1. Go to arxiv.org and navigate to the Advanced Search page.\\n2. Enter \"AI regulation\" in the search box and select \"All fields\" from the dropdown.\\n3. Enter 2022-06-01 and 2022-07-01 into the date inputs, select \"Submission date (original)\", and submit the search.\\n4. Go through the search results to find the article that has a figure with three axes and labels on each end of the axes, titled \"Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulation\".\\n5. Note the six words used as labels: deontological, egalitarian, localized, standardized, utilitarian, and consequential.\\n6. Go back to arxiv.org\\n7. Find \"Physics and Society\" and go to the page for the \"Physics and Society\" category.\\n8. Note that the tag for this category is \"physics.soc-ph\".\\n9. Go to the Advanced Search page.\\n10. Enter \"physics.soc-ph\" in the search box and select \"All fields\" from the dropdown.\\n11. Enter 2016-08-11 and 2016-08-12 into the date inputs, select \"Submission date (original)\", and submit the search.\\n12. Search for instances of the six words in the results to find the paper titled \"Phase transition from egalitarian to hierarchical societies driven by competition between cognitive and social constraints\", indicating that \"egalitarian\" is the correct answer.',\n",
              "  'Number of steps': '12',\n",
              "  'How long did this take?': '8 minutes',\n",
              "  'Tools': '1. Web browser\\n2. Image recognition tools (to identify and parse a figure with three axes)',\n",
              "  'Number of tools': '2'}}"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "json_QA[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wmFYIC2ec_FU",
        "outputId": "bd7f0444-02f3-4521-dc14-32faaf8abaec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==================================================\n",
            "Task ID: 872bfbb1-9ccf-49f6-8c5f-aa22818ccd66\n",
            "Question: Which of the fruits shown in the 2008 painting \"Embroidery from Uzbekistan\" were served as part of the October 1949 breakfast menu for the ocean liner that was later used as a floating prop for the film \"The Last Voyage\"? Give the items as a comma-separated list, ordering them in clockwise order based on their arrangement in the painting starting from the 12 o'clock position. Use the plural form of each fruit.\n",
            "Level: 3\n",
            "Final Answer: pears, bananas\n",
            "Annotator Metadata: \n",
            "  ├── Steps: \n",
            "  │      ├── 1. Use search engine to search for \"2008 painting Embroidery from Uzbekistan\".\n",
            "  │      ├── 2. Open the top result, a link to the painting's page on the Dayton Art Institute website, and verify that the painting has the specified title and year.\n",
            "  │      ├── 3. Identify the fruits in the painting as watermelon, pear, lemon, and banana, which can be verified by either watching the video on the page or reading its linked transcript.\n",
            "  │      ├── 4. Use search engine to search for \"ocean liner floating prop The Last Voyage\".\n",
            "  │      ├── 5. Note from the results that this ocean liner was the SS Île de France.\n",
            "  │      ├── 6. Use search engine to search for \"October 1949 breakfast menu SS Île de France\".\n",
            "  │      ├── 7. Go to the result that shows the vintage SS Île de France breakfast menu for October 1949.\n",
            "  │      ├── 8. Search the menu for each of the four fruits from the painting, finding \"Pear\" and \"Bananas\" but no matches for \"lemon\" or \"watermelon\".\n",
            "  │      ├── 9. Check the positions of the fruits in the painting to find that the pears come before the bananas in clockwise order starting from the 12 o'clock position.\n",
            "  │      ├── 10. Format the final answer as specified using the correct ordering: pears, bananas\n",
            "  ├── Number of steps: 10\n",
            "  ├── How long did this take?: 6\n",
            "  ├── Tools:\n",
            "  │      ├── 1. Web browser\n",
            "  │      ├── 2. Search engine\n",
            "  │      ├── 3. Image recognition and processing tools\n",
            "  └── Number of tools: 3\n",
            "==================================================\n",
            "Task ID: 1dcc160f-c187-48c2-b68e-319bd4354f3d\n",
            "Question: According to Openreview.net, at the NeurIPS 2022 Conference, how many papers by an author named Yuri were accepted with a \"certain\" recommendation?\n",
            "Level: 2\n",
            "Final Answer: 3\n",
            "Annotator Metadata: \n",
            "  ├── Steps: \n",
            "  │      ├── 1. Went to openreview.net.\n",
            "  │      ├── 2. Scroll down and clicked the \"All venues\" link.\n",
            "  │      ├── 3. Clicked \"NeurIPS\".\n",
            "  │      ├── 4. Opened the \"2022\" toggle menu.\n",
            "  │      ├── 5. Clicked \"NeurIPS 2022 Conference\".\n",
            "  │      ├── 6. Opened the top paper.\n",
            "  │      ├── 7. Clicked \"Go to NeurIPS 2022 Conference homepage\".\n",
            "  │      ├── 8. Searched \"Yuri\" in the search box.\n",
            "  │      ├── 9. Opened each of the four papers and checked the Recommendation field.\n",
            "  │      ├── 10. Counted the \"Certain\" recommendations.\n",
            "  ├── Number of steps: 8\n",
            "  ├── How long did this take?: 10 minutes\n",
            "  ├── Tools:\n",
            "  │      ├── 1. Web browser\n",
            "  │      ├── 2. Search engine\n",
            "  └── Number of tools: 2\n",
            "==================================================\n",
            "Task ID: 73c1b9fe-ee1d-4cf4-96ca-35c08f97b054\n",
            "Question: According to the USGS, in what year was the American Alligator first found west of Texas (not including Texas)?\n",
            "Level: 2\n",
            "Final Answer: 1954\n",
            "Annotator Metadata: \n",
            "  ├── Steps: \n",
            "  │      ├── 1. Search the web for “American Alligator USGS”.\n",
            "  │      ├── 2. Click result for the USGS Species Profile.\n",
            "  │      ├── 3. Click “Animated Map”.\n",
            "  │      ├── 4. Click the “Skip years with no recorded sightings” button.\n",
            "  │      ├── 5. Zoom out on the map to better view the whole U.S.\n",
            "  │      ├── 6. Move the slider back to the beginning, then advance it until I see a red dot pop up west of Texas.\n",
            "  │      ├── 7. Note the year that the dot appears, 1954.\n",
            "  ├── Number of steps: 7\n",
            "  ├── How long did this take?: 5 minutes\n",
            "  ├── Tools:\n",
            "  │      ├── 1. Search engine\n",
            "  │      ├── 2. Web browser\n",
            "  │      ├── 3. Image recognition\n",
            "  └── Number of tools: 3\n",
            "==================================================\n"
          ]
        }
      ],
      "source": [
        "import random\n",
        "\n",
        "# random.seed(42)\n",
        "\n",
        "# Randomly selecting 3 tasks\n",
        "random_samples = random.sample(json_QA, 3)\n",
        "for sample in random_samples:\n",
        "    print(\"=\" * 50)\n",
        "    print(f\"Task ID: {sample['task_id']}\")\n",
        "    print(f\"Question: {sample['Question']}\")\n",
        "    print(f\"Level: {sample['Level']}\")\n",
        "    print(f\"Final Answer: {sample['Final answer']}\")\n",
        "    print(f\"Annotator Metadata: \")\n",
        "    print(f\"  ├── Steps: \")\n",
        "    for step in sample['Annotator Metadata']['Steps'].split('\\n'):\n",
        "        print(f\"  │      ├── {step}\")\n",
        "    print(f\"  ├── Number of steps: {sample['Annotator Metadata']['Number of steps']}\")\n",
        "    print(f\"  ├── How long did this take?: {sample['Annotator Metadata']['How long did this take?']}\")\n",
        "    print(f\"  ├── Tools:\")\n",
        "    for tool in sample['Annotator Metadata']['Tools'].split('\\n'):\n",
        "        print(f\"  │      ├── {tool}\")\n",
        "    print(f\"  └── Number of tools: {sample['Annotator Metadata']['Number of tools']}\")\n",
        "print(\"=\" * 50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MVLsbZq2hDxM",
        "outputId": "5728459c-1121-44f7-8105-2dcb6f3bc537"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "HUGGING_FACE_API:··········\n"
          ]
        }
      ],
      "source": [
        "import getpass\n",
        "import os\n",
        "\n",
        "if \"HUGGING_FACE_API\" not in os.environ:\n",
        "    os.environ[\"HUGGING_FACE_API\"] = getpass.getpass(\"HUGGING_FACE_API:\")"
      ]
    },
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        },
        "id": "hseoRWZxdBRO",
        "outputId": "7e6835f6-15bd-4f5e-9f62-e91691ce3669"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
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              "model_id": "08b5069e3fc047fa93cc8b9d25639047",
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              "version_minor": 0
            },
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "model_id": "74deb69ad55a4e988b3539299af407ba",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "README.md:   0%|          | 0.00/10.4k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "model_id": "b6683bace3a3499e895409fc3ede24a6",
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              "version_minor": 0
            },
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
            ]
          },
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          "output_type": "display_data"
        },
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              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/571 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n",
            "WARNING:huggingface_hub.file_download:Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n"
          ]
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            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/438M [00:00<?, ?B/s]"
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        },
        {
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              "model_id": "56cf15aa0bc3429c896bfbef84a78d3c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/363 [00:00<?, ?B/s]"
            ]
          },
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        },
        {
          "data": {
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              "model_id": "1009f9eb518543a380960e580c3dc43d",
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              "version_minor": 0
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            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
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        {
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              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
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          },
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          "output_type": "display_data"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fb51f0ae287a4d658acffb3fc5dfa239",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/239 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8eb48bf59c5d47e5a0b94f7536bb7606",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import os\n",
        "from dotenv import load_dotenv\n",
        "from langchain_huggingface import HuggingFaceEmbeddings\n",
        "from langchain_chroma import Chroma\n",
        "\n",
        "\n",
        "embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\") #  dim=768\n",
        "\n",
        "vector_store = Chroma(\n",
        "    collection_name=\"example_collection\",\n",
        "    embedding_function=embeddings,\n",
        "    persist_directory=\"./chroma_langchain_db\",  # Where to save data locally, remove if not necessary\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "1qHqQA-7f8sj"
      },
      "outputs": [],
      "source": [
        "from langchain.schema import Document\n",
        "\n",
        "docs = []\n",
        "for sample in json_QA:\n",
        "    content = f\"Question: {sample['Question']}\\n\\nFinal answer: {sample['Final answer']}\"\n",
        "    doc = Document(\n",
        "        page_content=content,\n",
        "        metadata={\"source\": sample[\"task_id\"]}\n",
        "    )\n",
        "    docs.append(doc)\n",
        "\n",
        "# Upload the documents to the vector store\n",
        "try:\n",
        "    vector_store.add_documents(docs)\n",
        "except Exception as exception:\n",
        "    print(\"Error inserting data into Chroma vector store:\", exception)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "v4ALfgdOjfLx"
      },
      "outputs": [],
      "source": [
        "# ALTERNATIVE : Save the documents (a list of dict) into a csv file, and manually upload it to chroma\n",
        "import pandas as pd\n",
        "df = pd.DataFrame(docs)\n",
        "df.to_csv('chroma_docs.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "-MV_Ux69kdk-",
        "outputId": "8845afc9-ed8c-489f-ac6f-60ac212ebbbf"
      },
      "outputs": [
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              "      <th>1</th>\n",
              "      <td>(id, None)</td>\n",
              "      <td>(metadata, {'source': '17b5a6a3-bc87-42e8-b0fb...</td>\n",
              "      <td>(page_content, Question: I’m researching speci...</td>\n",
              "      <td>(type, Document)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(id, None)</td>\n",
              "      <td>(metadata, {'source': '04a04a9b-226c-43fd-b319...</td>\n",
              "      <td>(page_content, Question: If we assume all arti...</td>\n",
              "      <td>(type, Document)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(id, None)</td>\n",
              "      <td>(metadata, {'source': '14569e28-c88c-43e4-8c32...</td>\n",
              "      <td>(page_content, Question: In Unlambda, what exa...</td>\n",
              "      <td>(type, Document)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(id, None)</td>\n",
              "      <td>(metadata, {'source': 'e1fc63a2-da7a-432f-be78...</td>\n",
              "      <td>(page_content, Question: If Eliud Kipchoge cou...</td>\n",
              "      <td>(type, Document)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b35c9d73-ea10-4418-a80d-e4de130f6bc5')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b35c9d73-ea10-4418-a80d-e4de130f6bc5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b35c9d73-ea10-4418-a80d-e4de130f6bc5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-ee038bd6-631d-44ac-95f7-3b075970c0b7\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ee038bd6-631d-44ac-95f7-3b075970c0b7')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-ee038bd6-631d-44ac-95f7-3b075970c0b7 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "            0                                                  1  \\\n",
              "0  (id, None)  (metadata, {'source': 'c61d22de-5f6c-4958-a7f6...   \n",
              "1  (id, None)  (metadata, {'source': '17b5a6a3-bc87-42e8-b0fb...   \n",
              "2  (id, None)  (metadata, {'source': '04a04a9b-226c-43fd-b319...   \n",
              "3  (id, None)  (metadata, {'source': '14569e28-c88c-43e4-8c32...   \n",
              "4  (id, None)  (metadata, {'source': 'e1fc63a2-da7a-432f-be78...   \n",
              "\n",
              "                                                   2                 3  \n",
              "0  (page_content, Question: A paper about AI regu...  (type, Document)  \n",
              "1  (page_content, Question: I’m researching speci...  (type, Document)  \n",
              "2  (page_content, Question: If we assume all arti...  (type, Document)  \n",
              "3  (page_content, Question: In Unlambda, what exa...  (type, Document)  \n",
              "4  (page_content, Question: If Eliud Kipchoge cou...  (type, Document)  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "EDq_10tukeXs"
      },
      "outputs": [],
      "source": [
        "# Create retriever from the vector store\n",
        "retriever = vector_store.as_retriever()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gm-yrx-wk2yP",
        "outputId": "b0a1771b-6439-432e-9793-71f807b18f8f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Document(id='1c2af485-4a1b-402f-a872-088690f810ad', metadata={'source': '840bfca7-4f7b-481a-8794-c560c340185d'}, page_content='Question: On June 6, 2023, an article by Carolyn Collins Petersen was published in Universe Today. This article mentions a team that produced a paper about their observations, linked at the bottom of the article. Find this paper. Under what NASA award number was the work performed by R. G. Arendt supported by?\\n\\nFinal answer: 80GSFC21M0002')"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "query = \"On June 6, 2023, an article by Carolyn Collins Petersen was published in Universe Today. This article mentions a team that produced a paper about their observations, linked at the bottom of the article. Find this paper. Under what NASA award number was the work performed by R. G. Arendt supported by?\"\n",
        "matched_docs = vector_store.similarity_search(query, 2)\n",
        "docs = retriever.invoke(query)\n",
        "docs[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oI6Op4DGk7Al",
        "outputId": "d445b5f4-4034-46dd-a231-353cb7dfaded"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[Document(id='1c2af485-4a1b-402f-a872-088690f810ad', metadata={'source': '840bfca7-4f7b-481a-8794-c560c340185d'}, page_content='Question: On June 6, 2023, an article by Carolyn Collins Petersen was published in Universe Today. This article mentions a team that produced a paper about their observations, linked at the bottom of the article. Find this paper. Under what NASA award number was the work performed by R. G. Arendt supported by?\\n\\nFinal answer: 80GSFC21M0002'),\n",
              " Document(id='ae554bfa-3de0-4c17-b8ba-765f58abbf37', metadata={'source': 'a7feb290-76bb-4cb7-8800-7edaf7954f2f'}, page_content='Question: How many High Energy Physics - Lattice articles listed in January 2020 on Arxiv had ps versions available?\\n\\nFinal answer: 31')]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "matched_docs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wdB5lLaolTqk",
        "outputId": "0698959f-8f0d-494e-d9fd-950a46b77d5f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "List of tools used in all samples:\n",
            "Total number of tools used: 83\n",
            "  ├── web browser: 107\n",
            "  ├── image recognition tools (to identify and parse a figure with three axes): 1\n",
            "  ├── search engine: 101\n",
            "  ├── calculator: 34\n",
            "  ├── unlambda compiler (optional): 1\n",
            "  ├── a web browser.: 2\n",
            "  ├── a search engine.: 2\n",
            "  ├── a calculator.: 1\n",
            "  ├── microsoft excel: 5\n",
            "  ├── google search: 1\n",
            "  ├── ne: 9\n",
            "  ├── pdf access: 7\n",
            "  ├── file handling: 2\n",
            "  ├── python: 3\n",
            "  ├── image recognition tools: 12\n",
            "  ├── jsonld file access: 1\n",
            "  ├── video parsing: 1\n",
            "  ├── python compiler: 1\n",
            "  ├── video recognition tools: 3\n",
            "  ├── pdf viewer: 7\n",
            "  ├── microsoft excel / google sheets: 3\n",
            "  ├── word document access: 1\n",
            "  ├── tool to extract text from images: 1\n",
            "  ├── a word reversal tool / script: 1\n",
            "  ├── counter: 1\n",
            "  ├── excel: 3\n",
            "  ├── image recognition: 5\n",
            "  ├── color recognition: 3\n",
            "  ├── excel file access: 3\n",
            "  ├── xml file access: 1\n",
            "  ├── access to the internet archive, web.archive.org: 1\n",
            "  ├── text processing/diff tool: 1\n",
            "  ├── gif parsing tools: 1\n",
            "  ├── a web browser: 7\n",
            "  ├── a search engine: 7\n",
            "  ├── a speech-to-text tool: 2\n",
            "  ├── code/data analysis tools: 1\n",
            "  ├── audio capability: 2\n",
            "  ├── pdf reader: 1\n",
            "  ├── markdown: 1\n",
            "  ├── a calculator: 5\n",
            "  ├── access to wikipedia: 3\n",
            "  ├── image recognition/ocr: 3\n",
            "  ├── google translate access: 1\n",
            "  ├── ocr: 4\n",
            "  ├── bass note data: 1\n",
            "  ├── text editor: 1\n",
            "  ├── xlsx file access: 1\n",
            "  ├── powerpoint viewer: 1\n",
            "  ├── csv file access: 1\n",
            "  ├── calculator (or use excel): 1\n",
            "  ├── computer algebra system: 1\n",
            "  ├── video processing software: 1\n",
            "  ├── audio processing software: 1\n",
            "  ├── computer vision: 1\n",
            "  ├── google maps: 1\n",
            "  ├── access to excel files: 1\n",
            "  ├── calculator (or ability to count): 1\n",
            "  ├── a file interface: 3\n",
            "  ├── a python ide: 1\n",
            "  ├── spreadsheet editor: 1\n",
            "  ├── tools required: 1\n",
            "  ├── b browser: 1\n",
            "  ├── image recognition and processing tools: 1\n",
            "  ├── computer vision or ocr: 1\n",
            "  ├── c++ compiler: 1\n",
            "  ├── access to google maps: 1\n",
            "  ├── youtube player: 1\n",
            "  ├── natural language processor: 1\n",
            "  ├── graph interaction tools: 1\n",
            "  ├── bablyonian cuniform -> arabic legend: 1\n",
            "  ├── access to youtube: 1\n",
            "  ├── image search tools: 1\n",
            "  ├── calculator or counting function: 1\n",
            "  ├── a speech-to-text audio processing tool: 1\n",
            "  ├── access to academic journal websites: 1\n",
            "  ├── pdf reader/extracter: 1\n",
            "  ├── rubik's cube model: 1\n",
            "  ├── wikipedia: 1\n",
            "  ├── video capability: 1\n",
            "  ├── image processing tools: 1\n",
            "  ├── age recognition software: 1\n",
            "  ├── youtube: 1\n"
          ]
        }
      ],
      "source": [
        "from collections import Counter, OrderedDict\n",
        "\n",
        "tools = []\n",
        "for sample in json_QA:\n",
        "  for tool in sample['Annotator Metadata']['Tools'].split('\\n'):\n",
        "    tool = tool[2:].strip().lower()\n",
        "    if tool.startswith(\"(\"):\n",
        "      tool = tool[11:].strip()\n",
        "    tools.append(tool)\n",
        "\n",
        "tools_counter = OrderedDict(Counter(tools))\n",
        "print(\"List of tools used in all samples:\")\n",
        "print(\"Total number of tools used:\", len(tools_counter))\n",
        "for tool, count in tools_counter.items():\n",
        "    print(f\"  ├── {tool}: {count}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "6tFba9MYl_78"
      },
      "outputs": [],
      "source": [
        "system_prompt = \"\"\"\n",
        "You are a general AI assistant. I will ask you a question. Report your thoughts,\n",
        "and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER].\n",
        "YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of\n",
        "numbers and/or strings. If you are asked for a number, don't use comma to write your number neither\n",
        "use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles,\n",
        "neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for\n",
        "a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.\n",
        "\n",
        "You need to provide a step-by-step explanation of how you arrived at the answer.\n",
        "==========================\n",
        "You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.\n",
        "\n",
        "Here is a few examples showing you how to answer the question step by step.\n",
        "\"\"\"\n",
        "for i, samples in enumerate(random_samples):\n",
        "    system_prompt += f\"\\nQuestion {i+1}: {samples['Question']}\\nSteps:\\n{samples['Annotator Metadata']['Steps']}\\nTools:\\n{samples['Annotator Metadata']['Tools']}\\nFinal Answer: {samples['Final answer']}\\n\"\n",
        "system_prompt += \"\\n==========================\\n\"\n",
        "system_prompt += \"Now, please answer the following question step by step.\\n\"\n",
        "\n",
        "# save the system_prompt to a file\n",
        "with open('system_prompt.txt', 'w') as f:\n",
        "    f.write(system_prompt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WarEUlNBmEtj",
        "outputId": "5989465a-4502-40e4-af78-097ffd2ce8e6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "You are a general AI assistant. I will ask you a question. Report your thoughts,\n",
            "and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER].\n",
            "YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of \n",
            "numbers and/or strings. If you are asked for a number, don't use comma to write your number neither\n",
            "use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles,\n",
            "neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for\n",
            "a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.\n",
            "\n",
            "You need to provide a step-by-step explanation of how you arrived at the answer.\n",
            "==========================\n",
            "You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.\n",
            "\n",
            "Here is a few examples showing you how to answer the question step by step.\n",
            "\n",
            "Question 1: Which of the fruits shown in the 2008 painting \"Embroidery from Uzbekistan\" were served as part of the October 1949 breakfast menu for the ocean liner that was later used as a floating prop for the film \"The Last Voyage\"? Give the items as a comma-separated list, ordering them in clockwise order based on their arrangement in the painting starting from the 12 o'clock position. Use the plural form of each fruit.\n",
            "Steps:\n",
            "1. Use search engine to search for \"2008 painting Embroidery from Uzbekistan\".\n",
            "2. Open the top result, a link to the painting's page on the Dayton Art Institute website, and verify that the painting has the specified title and year.\n",
            "3. Identify the fruits in the painting as watermelon, pear, lemon, and banana, which can be verified by either watching the video on the page or reading its linked transcript.\n",
            "4. Use search engine to search for \"ocean liner floating prop The Last Voyage\".\n",
            "5. Note from the results that this ocean liner was the SS Île de France.\n",
            "6. Use search engine to search for \"October 1949 breakfast menu SS Île de France\".\n",
            "7. Go to the result that shows the vintage SS Île de France breakfast menu for October 1949.\n",
            "8. Search the menu for each of the four fruits from the painting, finding \"Pear\" and \"Bananas\" but no matches for \"lemon\" or \"watermelon\".\n",
            "9. Check the positions of the fruits in the painting to find that the pears come before the bananas in clockwise order starting from the 12 o'clock position.\n",
            "10. Format the final answer as specified using the correct ordering: pears, bananas\n",
            "Tools:\n",
            "1. Web browser\n",
            "2. Search engine\n",
            "3. Image recognition and processing tools\n",
            "Final Answer: pears, bananas\n",
            "\n",
            "Question 2: According to Openreview.net, at the NeurIPS 2022 Conference, how many papers by an author named Yuri were accepted with a \"certain\" recommendation?\n",
            "Steps:\n",
            "1. Went to openreview.net.\n",
            "2. Scroll down and clicked the \"All venues\" link.\n",
            "3. Clicked \"NeurIPS\".\n",
            "4. Opened the \"2022\" toggle menu.\n",
            "5. Clicked \"NeurIPS 2022 Conference\".\n",
            "6. Opened the top paper.\n",
            "7. Clicked \"Go to NeurIPS 2022 Conference homepage\".\n",
            "8. Searched \"Yuri\" in the search box.\n",
            "9. Opened each of the four papers and checked the Recommendation field.\n",
            "10. Counted the \"Certain\" recommendations.\n",
            "Tools:\n",
            "1. Web browser\n",
            "2. Search engine\n",
            "Final Answer: 3\n",
            "\n",
            "Question 3: According to the USGS, in what year was the American Alligator first found west of Texas (not including Texas)?\n",
            "Steps:\n",
            "1. Search the web for “American Alligator USGS”.\n",
            "2. Click result for the USGS Species Profile.\n",
            "3. Click “Animated Map”.\n",
            "4. Click the “Skip years with no recorded sightings” button.\n",
            "5. Zoom out on the map to better view the whole U.S.\n",
            "6. Move the slider back to the beginning, then advance it until I see a red dot pop up west of Texas.\n",
            "7. Note the year that the dot appears, 1954.\n",
            "Tools:\n",
            "1. Search engine\n",
            "2. Web browser\n",
            "3. Image recognition\n",
            "Final Answer: 1954\n",
            "\n",
            "==========================\n",
            "Now, please answer the following question step by step.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# load the system prompt from the file\n",
        "with open('system_prompt.txt', 'r') as f:\n",
        "    system_prompt = f.read()\n",
        "print(system_prompt)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wgWWrrFtmGmw"
      },
      "source": [
        "## Building Agent"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "NLzBvGF4avZ3"
      },
      "outputs": [],
      "source": [
        "import dotenv\n",
        "from langgraph.graph import MessagesState, START, StateGraph\n",
        "from langchain_google_genai import ChatGoogleGenerativeAI\n",
        "from langchain_huggingface import HuggingFaceEmbeddings\n",
        "from langchain_community.tools.tavily_search import TavilySearchResults\n",
        "from langchain_community.document_loaders import WikipediaLoader\n",
        "from langchain_community.document_loaders import ArxivLoader\n",
        "from langchain_chroma import Chroma\n",
        "from langchain.agents import initialize_agent, Tool\n",
        "from langchain.tools.retriever import create_retriever_tool\n",
        "from langchain_core.tools import tool\n",
        "\n",
        "\n",
        "load_dotenv()\n",
        "\n",
        "embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\") #  dim=768\n",
        "\n",
        "vector_store = Chroma(\n",
        "    collection_name=\"example_collection\",\n",
        "    embedding_function=embeddings,\n",
        "    persist_directory=\"./chroma_langchain_db_1\",  # Where to save data locally, remove if not necessary\n",
        ")\n",
        "# Get the retriever object from the vector store using as_retriever()\n",
        "retriever = vector_store.as_retriever()\n",
        "\n",
        "question_retrivel_tool = create_retriever_tool(\n",
        "    retriever, # Pass the retriever object to create_retriever_tool\n",
        "    \"Question Retriever\",\n",
        "    \" Find the similar questions in the vector database for thr given question.\",\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "zYNW2il3avUK"
      },
      "outputs": [],
      "source": [
        "@tool\n",
        "def wikipedia_search(query: str) -> str:\n",
        "  \"\"\"Search wikipedia for the query\"\"\"\n",
        "  search_docs = WikipediaLoader(query=query, load_max_docs=2).load()\n",
        "  formatted_search_docs = \"\\n\\n---\\n\\n\".join(\n",
        "        [\n",
        "            f'<Document source=\"{doc.metadata[\"source\"]}\" page=\"{doc.metadata.get(\"page\", \"\")}\"/>\\n{doc.page_content}\\n</Document>'\n",
        "            for doc in search_docs\n",
        "        ])\n",
        "  return {\"wiki_results\": formatted_search_docs}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "SCQD2m_kavR0"
      },
      "outputs": [],
      "source": [
        "@tool\n",
        "def web_search(query: str) -> str:\n",
        "  \"\"\"Search web for the query\"\"\"\n",
        "  search_docs = TavilySearchResults(max_results=3).invoke(query=query)\n",
        "  formatted_search_docs = \"\\n\\n---\\n\\n\".join(\n",
        "        [\n",
        "            f'<Document source=\"{doc.metadata[\"source\"]}\" page=\"{doc.metadata.get(\"page\", \"\")}\"/>\\n{doc.page_content}\\n</Document>'\n",
        "            for doc in search_docs\n",
        "        ])\n",
        "  return {\"web_results\": formatted_search_docs}\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "MY_5ZUNCavPV"
      },
      "outputs": [],
      "source": [
        "@tool\n",
        "def arvix_search(query: str) -> str:\n",
        "    \"\"\"Search Arxiv for a query and return maximum 3 result.\n",
        "\n",
        "    Args:\n",
        "        query: The search query.\"\"\"\n",
        "    search_docs = ArxivLoader(query=query, load_max_docs=3).load()\n",
        "    formatted_search_docs = \"\\n\\n---\\n\\n\".join(\n",
        "        [\n",
        "            f'<Document source=\"{doc.metadata[\"source\"]}\" page=\"{doc.metadata.get(\"page\", \"\")}\"/>\\n{doc.page_content[:1000]}\\n</Document>'\n",
        "            for doc in search_docs\n",
        "        ])\n",
        "    return {\"arvix_results\": formatted_search_docs}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "Yr9dP5pFavNA"
      },
      "outputs": [],
      "source": [
        "from langchain_core.messages import SystemMessage, HumanMessage\n",
        "\n",
        "\n",
        "# load the system prompt from the file\n",
        "with open(\"system_prompt.txt\", \"r\", encoding=\"utf-8\") as f:\n",
        "    system_prompt = f.read()\n",
        "\n",
        "sys_msg = SystemMessage(content=system_prompt)\n",
        "\n",
        "embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\") #  dim=768\n",
        "\n",
        "vector_store = Chroma(\n",
        "    collection_name=\"example_collection\",\n",
        "    embedding_function=embeddings,\n",
        "    persist_directory=\"./chroma_langchain_db_1\",  # Where to save data locally, remove if not necessary\n",
        ")\n",
        "\n",
        "# Use langchain.tools.retriever.create_retriever_tool directly instead of the variable create_retriever_tool\n",
        "from langchain.tools.retriever import create_retriever_tool\n",
        "\n",
        "# Assign the result to a new variable name, like 'question_retriever_tool'\n",
        "question_retriever_tool = create_retriever_tool(\n",
        "    retriever=vector_store.as_retriever(),\n",
        "    name=\"question_search\",  # Changed name to be valid\n",
        "    description=\"A tool to retrieve similar questions from a vector store.\",\n",
        ")\n",
        "\n",
        "tools = [\n",
        "    wikipedia_search,\n",
        "    web_search,\n",
        "    arvix_search,\n",
        "    question_retriever_tool,\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "HHh1kgf9g4Tq"
      },
      "outputs": [],
      "source": [
        "from langgraph.prebuilt import ToolNode\n",
        "from langgraph.prebuilt import tools_condition\n",
        "\n",
        "# load the system prompt from the file\n",
        "with open('system_prompt.txt', 'r') as f:\n",
        "    system_prompt = f.read()\n",
        "\n",
        "\n",
        "# System message\n",
        "sys_msg = SystemMessage(content=system_prompt)\n",
        "\n",
        "llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\")\n",
        "llm_with_tools = llm.bind_tools(tools)\n",
        "\n",
        "\n",
        "# Node\n",
        "def assistant(state: MessagesState):\n",
        "    \"\"\"Assistant node\"\"\"\n",
        "    return {\"messages\": [llm_with_tools.invoke([sys_msg] + state[\"messages\"])]}\n",
        "\n",
        "# Build graph\n",
        "builder = StateGraph(MessagesState)\n",
        "builder.add_node(\"assistant\", assistant)\n",
        "builder.add_node(\"tools\", ToolNode(tools))\n",
        "builder.add_edge(START, \"assistant\")\n",
        "builder.add_conditional_edges(\n",
        "    \"assistant\",\n",
        "    # If the latest message (result) from assistant is a tool call -> tools_condition routes to tools\n",
        "    # If the latest message (result) from assistant is a not a tool call -> tools_condition routes to END\n",
        "    tools_condition,\n",
        ")\n",
        "builder.add_edge(\"tools\", \"assistant\")\n",
        "\n",
        "# Compile graph\n",
        "graph = builder.compile()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "d7TcxuechEPs",
        "outputId": "9b3c24c7-91ea-427a-8af2-5d9214c4f64f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from IPython.display import Image, display\n",
        "\n",
        "display(Image(graph.get_graph(xray=True).draw_mermaid_png()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
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            "================================\u001b[1m Human Message \u001b[0m=================================\n",
            "\n",
            "What is the capital of France?\n",
            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
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            "I know the capital of France.\n",
            "FINAL ANSWER: Paris\n"
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