vanna_sqlite_nba_shots.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sugarforever/OpenAI-Tutorials/blob/main/vanna_sqlite_nba_shots.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AdzbfEJl6t4b",
        "outputId": "d148a16b-4f9b-483c-ac28-786a406c343f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89.9/89.9 kB\u001b[0m \u001b[31m3.1 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[31m38.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.3/67.3 kB\u001b[0m \u001b[31m940.2 kB/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[32m50.4/50.4 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.1/13.1 MB\u001b[0m \u001b[31m54.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m602.6/602.6 kB\u001b[0m \u001b[31m25.9 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[31m49.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.9/79.9 MB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m383.7/383.7 kB\u001b[0m \u001b[31m20.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m198.2/198.2 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m273.8/273.8 kB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m94.6/94.6 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.4/76.4 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.0/78.0 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[32m325.2/325.2 kB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m41.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m93.2/93.2 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.2/13.2 MB\u001b[0m \u001b[31m39.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.5/52.5 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m141.9/141.9 kB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.4/54.4 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.7/63.7 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[32m58.3/58.3 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m341.4/341.4 kB\u001b[0m \u001b[31m19.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.5/71.5 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m60.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m425.7/425.7 kB\u001b[0m \u001b[31m21.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m164.1/164.1 kB\u001b[0m \u001b[31m10.5 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[31m2.8 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[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for flasgger (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.6.1 requires pandas<2.2.3dev0,>=2.0, but you have pandas 2.2.3 which is incompatible.\n",
            "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.2.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install 'vanna[chromadb,openai]' pandas -qU"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import sqlite3"
      ],
      "metadata": {
        "id": "jQtPsdus7YZs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def import_csv_to_sqlite(csv_path, db_name='nba_shots.db'):\n",
        "    df = pd.read_csv(csv_path)\n",
        "    conn = sqlite3.connect(db_name)\n",
        "    df.to_sql('nba_shots', conn, if_exists='replace', index=False)\n",
        "    conn.close()\n",
        "\n",
        "    print(f\"Data imported successfully to {db_name}\")"
      ],
      "metadata": {
        "id": "i0Y9M-me7XPu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import_csv_to_sqlite('NBA_2024_Shots.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h1HVpmm27nJu",
        "outputId": "91e4c50c-134e-4335-d44a-8b41f18a6819"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data imported successfully to nba_shots.db\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from vanna.openai import OpenAI_Chat\n",
        "from vanna.chromadb import ChromaDB_VectorStore"
      ],
      "metadata": {
        "id": "_3dEeKxl8atQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import userdata\n",
        "OPENAI_API_KEY = userdata.get('OPENAI_API_KEY')"
      ],
      "metadata": {
        "id": "VOeluHT68hZZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class MyVanna(ChromaDB_VectorStore, OpenAI_Chat):\n",
        "    def __init__(self, config=None):\n",
        "        ChromaDB_VectorStore.__init__(self, config=config)\n",
        "        OpenAI_Chat.__init__(self, config=config)\n",
        "\n",
        "vn = MyVanna(config={'api_key': OPENAI_API_KEY, 'model': 'gpt-4o'})"
      ],
      "metadata": {
        "id": "XanSd_cw8d0W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vn.connect_to_sqlite('nba_shots.db')"
      ],
      "metadata": {
        "id": "A-5wuULi88zM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "documentation = '''\n",
        "SEASON_1 & SEASON_2: Season indicator variables\n",
        "TEAM_ID: NBA's unique ID variable of that specific team in their API.\n",
        "PLAYER_ID: NBA's unique ID variable of that specific player in their API.\n",
        "PLAYER_NAME: Name of the player.\n",
        "GAME_DATE: Date of the game (M-D-Y // Month-Date-Year).\n",
        "GAME_ID: NBA's unique ID variable of that specific game in their API.\n",
        "EVENT_TYPE: Character variable denoting a shot outcome (Made Shot // Missed Shot).\n",
        "SHOT_MADE: True/False variable denoting a shot outcome (TRUE // FALSE).\n",
        "ACTION_TYPE: Description of shot type (layup, dunk, jump shot, etc.).\n",
        "SHOT_TYPE: Type of shot (2PT or 3PT).\n",
        "BASIC_ZONE: Name of the court zone the shot took place in.\n",
        "Restricted Area, In the Paint (non-RA), Midrange, Left Corner 3, Right Corner 3, Above the Break, Backcourt.\n",
        "ZONE_NAME: Name of the side of court the shot took place in.\n",
        "left, left side center, center, right side center, right\n",
        "ZONE_ABB: Abbreviation of the side of court.\n",
        "(L), (LC), (C), (RC), (R).\n",
        "ZONE_RANGE: Distance range of shot by zones.\n",
        "Less than 8 ft., 8-16 ft. 16-24 ft. 24+ ft.\n",
        "LOC_X: X coordinate of the shot in the x, y plane of the court (0, 50).\n",
        "LOC_Y: Y coordinate of the shot in the x, y plane of the court (0, 50).\n",
        "SHOT_DISTANCE: Distance of the shot with respect to the center of the hoop, in feet.\n",
        "QUARTER: Quarter of the game.\n",
        "MINS_LEFT: Minutes remaining in the quarter.\n",
        "SECS_LEFT: Seconds remaining in minute of the quarter.\n",
        "'''"
      ],
      "metadata": {
        "id": "T1GFO0sLn5_k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vn.train(documentation=f\"Please refer to the following content to understand the meaning of all the columns in the source of truth. {documentation}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "8oSRYDRzoBcu",
        "outputId": "61ffa2f3-7765-4e8b-8dde-c8bcbb41c891"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:chromadb.segment.impl.metadata.sqlite:Insert of existing embedding ID: f6aaf873-b43d-5013-b17c-19151c2ee38e-doc\n",
            "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Add of existing embedding ID: f6aaf873-b43d-5013-b17c-19151c2ee38e-doc\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Adding documentation....\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'f6aaf873-b43d-5013-b17c-19151c2ee38e-doc'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response = vn.ask(\"How many shots in total?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "n95lfqf69CAf",
        "outputId": "d09a8474-70e5-4233-cd7f-b56db4cd79c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Number of requested results 10 is greater than number of elements in index 2, updating n_results = 2\n",
            "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Number of requested results 10 is greater than number of elements in index 1, updating n_results = 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SQL Prompt: [{'role': 'system', 'content': \"You are a SQLite expert. Please help to generate a SQL query to answer the question. Your response should ONLY be based on the given context and follow the response guidelines and format instructions. \\n===Additional Context \\n\\nPlease refer to the following content to understand the meaning of all the columns in the source of truth. \\nSEASON_1 & SEASON_2: Season indicator variables\\nTEAM_ID: NBA's unique ID variable of that specific team in their API.\\nPLAYER_ID: NBA's unique ID variable of that specific player in their API.\\nPLAYER_NAME: Name of the player.\\nGAME_DATE: Date of the game (M-D-Y // Month-Date-Year).\\nGAME_ID: NBA's unique ID variable of that specific game in their API.\\nEVENT_TYPE: Character variable denoting a shot outcome (Made Shot // Missed Shot).\\nSHOT_MADE: True/False variable denoting a shot outcome (TRUE // FALSE).\\nACTION_TYPE: Description of shot type (layup, dunk, jump shot, etc.).\\nSHOT_TYPE: Type of shot (2PT or 3PT).\\nBASIC_ZONE: Name of the court zone the shot took place in.\\nRestricted Area, In the Paint (non-RA), Midrange, Left Corner 3, Right Corner 3, Above the Break, Backcourt.\\nZONE_NAME: Name of the side of court the shot took place in.\\nleft, left side center, center, right side center, right\\nZONE_ABB: Abbreviation of the side of court.\\n(L), (LC), (C), (RC), (R).\\nZONE_RANGE: Distance range of shot by zones.\\nLess than 8 ft., 8-16 ft. 16-24 ft. 24+ ft.\\nLOC_X: X coordinate of the shot in the x, y plane of the court (0, 50).\\nLOC_Y: Y coordinate of the shot in the x, y plane of the court (0, 50).\\nSHOT_DISTANCE: Distance of the shot with respect to the center of the hoop, in feet.\\nQUARTER: Quarter of the game.\\nMINS_LEFT: Minutes remaining in the quarter.\\nSECS_LEFT: Seconds remaining in minute of the quarter.\\n\\n\\n===Response Guidelines \\n1. If the provided context is sufficient, please generate a valid SQL query without any explanations for the question. \\n2. If the provided context is almost sufficient but requires knowledge of a specific string in a particular column, please generate an intermediate SQL query to find the distinct strings in that column. Prepend the query with a comment saying intermediate_sql \\n3. If the provided context is insufficient, please explain why it can't be generated. \\n4. Please use the most relevant table(s). \\n5. If the question has been asked and answered before, please repeat the answer exactly as it was given before. \\n6. Ensure that the output SQL is SQLite-compliant and executable, and free of syntax errors. \\n\"}, {'role': 'user', 'content': 'How many shots in total?'}, {'role': 'assistant', 'content': 'SELECT COUNT(*) FROM nba_shots'}, {'role': 'user', 'content': 'What are all the recorded shots in the NBA database?'}, {'role': 'assistant', 'content': 'SELECT * FROM nba_shots'}, {'role': 'user', 'content': 'How many shots in total?'}]\n",
            "Using model gpt-4o for 662.75 tokens (approx)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Add of existing embedding ID: dce14445-f079-5604-89c7-f29089905e5d-sql\n",
            "WARNING:chromadb.segment.impl.metadata.sqlite:Insert of existing embedding ID: dce14445-f079-5604-89c7-f29089905e5d-sql\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LLM Response: SELECT COUNT(*) FROM nba_shots\n",
            "SELECT COUNT(*) FROM nba_shots\n",
            "   COUNT(*)\n",
            "0    218701\n",
            "Using model gpt-4o for 158.0 tokens (approx)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gZi3yYVWjbbq",
        "outputId": "15600576-5aec-4ad9-c725-a7680b58aee4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('SELECT COUNT(*) FROM nba_shots',\n",
              "    COUNT(*)\n",
              " 0    218701,\n",
              " Figure({\n",
              "     'data': [{'mode': 'number', 'title': {'text': 'Total Shots'}, 'type': 'indicator', 'value': 218701}],\n",
              "     'layout': {'template': '...'}\n",
              " }))"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mTM4s-0d6t4f"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "source": [
        "question = \"Who made most shots? Give me the player name\""
      ],
      "metadata": {
        "id": "20HNDsdtonrR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vn.ask(question)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0T9Q---u9yAZ",
        "outputId": "364ca430-0b44-4f51-a3e9-b5e8626c1fdc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Number of requested results 10 is greater than number of elements in index 2, updating n_results = 2\n",
            "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Number of requested results 10 is greater than number of elements in index 1, updating n_results = 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SQL Prompt: [{'role': 'system', 'content': \"You are a SQLite expert. Please help to generate a SQL query to answer the question. Your response should ONLY be based on the given context and follow the response guidelines and format instructions. \\n===Additional Context \\n\\nPlease refer to the following content to understand the meaning of all the columns in the source of truth. \\nSEASON_1 & SEASON_2: Season indicator variables\\nTEAM_ID: NBA's unique ID variable of that specific team in their API.\\nPLAYER_ID: NBA's unique ID variable of that specific player in their API.\\nPLAYER_NAME: Name of the player.\\nGAME_DATE: Date of the game (M-D-Y // Month-Date-Year).\\nGAME_ID: NBA's unique ID variable of that specific game in their API.\\nEVENT_TYPE: Character variable denoting a shot outcome (Made Shot // Missed Shot).\\nSHOT_MADE: True/False variable denoting a shot outcome (TRUE // FALSE).\\nACTION_TYPE: Description of shot type (layup, dunk, jump shot, etc.).\\nSHOT_TYPE: Type of shot (2PT or 3PT).\\nBASIC_ZONE: Name of the court zone the shot took place in.\\nRestricted Area, In the Paint (non-RA), Midrange, Left Corner 3, Right Corner 3, Above the Break, Backcourt.\\nZONE_NAME: Name of the side of court the shot took place in.\\nleft, left side center, center, right side center, right\\nZONE_ABB: Abbreviation of the side of court.\\n(L), (LC), (C), (RC), (R).\\nZONE_RANGE: Distance range of shot by zones.\\nLess than 8 ft., 8-16 ft. 16-24 ft. 24+ ft.\\nLOC_X: X coordinate of the shot in the x, y plane of the court (0, 50).\\nLOC_Y: Y coordinate of the shot in the x, y plane of the court (0, 50).\\nSHOT_DISTANCE: Distance of the shot with respect to the center of the hoop, in feet.\\nQUARTER: Quarter of the game.\\nMINS_LEFT: Minutes remaining in the quarter.\\nSECS_LEFT: Seconds remaining in minute of the quarter.\\n\\n\\n===Response Guidelines \\n1. If the provided context is sufficient, please generate a valid SQL query without any explanations for the question. \\n2. If the provided context is almost sufficient but requires knowledge of a specific string in a particular column, please generate an intermediate SQL query to find the distinct strings in that column. Prepend the query with a comment saying intermediate_sql \\n3. If the provided context is insufficient, please explain why it can't be generated. \\n4. Please use the most relevant table(s). \\n5. If the question has been asked and answered before, please repeat the answer exactly as it was given before. \\n6. Ensure that the output SQL is SQLite-compliant and executable, and free of syntax errors. \\n\"}, {'role': 'user', 'content': 'What are all the recorded shots in the NBA database?'}, {'role': 'assistant', 'content': 'SELECT * FROM nba_shots'}, {'role': 'user', 'content': 'How many shots in total?'}, {'role': 'assistant', 'content': 'SELECT COUNT(*) FROM nba_shots'}, {'role': 'user', 'content': 'Who made most shots? Give me the player name'}]\n",
            "Using model gpt-4o for 667.75 tokens (approx)\n",
            "LLM Response: SELECT PLAYER_NAME \n",
            "FROM nba_shots \n",
            "GROUP BY PLAYER_NAME \n",
            "ORDER BY COUNT(*) DESC \n",
            "LIMIT 1;\n",
            "Extracted SQL: SELECT PLAYER_NAME \n",
            "FROM nba_shots \n",
            "GROUP BY PLAYER_NAME \n",
            "ORDER BY COUNT(*) DESC \n",
            "LIMIT 1;\n",
            "SELECT PLAYER_NAME \n",
            "FROM nba_shots \n",
            "GROUP BY PLAYER_NAME \n",
            "ORDER BY COUNT(*) DESC \n",
            "LIMIT 1;\n",
            "   PLAYER_NAME\n",
            "0  Luka Doncic\n",
            "Using model gpt-4o for 179.0 tokens (approx)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('SELECT PLAYER_NAME \\nFROM nba_shots \\nGROUP BY PLAYER_NAME \\nORDER BY COUNT(*) DESC \\nLIMIT 1;',\n",
              "    PLAYER_NAME\n",
              " 0  Luka Doncic,\n",
              " Figure({\n",
              "     'data': [{'mode': 'number', 'title': {'text': 'Luka Doncic'}, 'type': 'indicator', 'value': 1}],\n",
              "     'layout': {'template': '...'}\n",
              " }))"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sql = vn.generate_sql(question)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oz0X9n6eou1J",
        "outputId": "97acb89a-bc83-41d5-d2ca-49e23e1f33c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Number of requested results 10 is greater than number of elements in index 3, updating n_results = 3\n",
            "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Number of requested results 10 is greater than number of elements in index 1, updating n_results = 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SQL Prompt: [{'role': 'system', 'content': \"You are a SQLite expert. Please help to generate a SQL query to answer the question. Your response should ONLY be based on the given context and follow the response guidelines and format instructions. \\n===Additional Context \\n\\nPlease refer to the following content to understand the meaning of all the columns in the source of truth. \\nSEASON_1 & SEASON_2: Season indicator variables\\nTEAM_ID: NBA's unique ID variable of that specific team in their API.\\nPLAYER_ID: NBA's unique ID variable of that specific player in their API.\\nPLAYER_NAME: Name of the player.\\nGAME_DATE: Date of the game (M-D-Y // Month-Date-Year).\\nGAME_ID: NBA's unique ID variable of that specific game in their API.\\nEVENT_TYPE: Character variable denoting a shot outcome (Made Shot // Missed Shot).\\nSHOT_MADE: True/False variable denoting a shot outcome (TRUE // FALSE).\\nACTION_TYPE: Description of shot type (layup, dunk, jump shot, etc.).\\nSHOT_TYPE: Type of shot (2PT or 3PT).\\nBASIC_ZONE: Name of the court zone the shot took place in.\\nRestricted Area, In the Paint (non-RA), Midrange, Left Corner 3, Right Corner 3, Above the Break, Backcourt.\\nZONE_NAME: Name of the side of court the shot took place in.\\nleft, left side center, center, right side center, right\\nZONE_ABB: Abbreviation of the side of court.\\n(L), (LC), (C), (RC), (R).\\nZONE_RANGE: Distance range of shot by zones.\\nLess than 8 ft., 8-16 ft. 16-24 ft. 24+ ft.\\nLOC_X: X coordinate of the shot in the x, y plane of the court (0, 50).\\nLOC_Y: Y coordinate of the shot in the x, y plane of the court (0, 50).\\nSHOT_DISTANCE: Distance of the shot with respect to the center of the hoop, in feet.\\nQUARTER: Quarter of the game.\\nMINS_LEFT: Minutes remaining in the quarter.\\nSECS_LEFT: Seconds remaining in minute of the quarter.\\n\\n\\n===Response Guidelines \\n1. If the provided context is sufficient, please generate a valid SQL query without any explanations for the question. \\n2. If the provided context is almost sufficient but requires knowledge of a specific string in a particular column, please generate an intermediate SQL query to find the distinct strings in that column. Prepend the query with a comment saying intermediate_sql \\n3. If the provided context is insufficient, please explain why it can't be generated. \\n4. Please use the most relevant table(s). \\n5. If the question has been asked and answered before, please repeat the answer exactly as it was given before. \\n6. Ensure that the output SQL is SQLite-compliant and executable, and free of syntax errors. \\n\"}, {'role': 'user', 'content': 'Who made most shots? Give me the player name'}, {'role': 'assistant', 'content': 'SELECT PLAYER_NAME \\nFROM nba_shots \\nGROUP BY PLAYER_NAME \\nORDER BY COUNT(*) DESC \\nLIMIT 1;'}, {'role': 'user', 'content': 'What are all the recorded shots in the NBA database?'}, {'role': 'assistant', 'content': 'SELECT * FROM nba_shots'}, {'role': 'user', 'content': 'How many shots in total?'}, {'role': 'assistant', 'content': 'SELECT COUNT(*) FROM nba_shots'}, {'role': 'user', 'content': 'Who made most shots? Give me the player name'}]\n",
            "Using model gpt-4o for 701.25 tokens (approx)\n",
            "LLM Response: SELECT PLAYER_NAME \n",
            "FROM nba_shots \n",
            "GROUP BY PLAYER_NAME \n",
            "ORDER BY COUNT(*) DESC \n",
            "LIMIT 1;\n",
            "Extracted SQL: SELECT PLAYER_NAME \n",
            "FROM nba_shots \n",
            "GROUP BY PLAYER_NAME \n",
            "ORDER BY COUNT(*) DESC \n",
            "LIMIT 1;\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sql"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "9FC75_6noyGV",
        "outputId": "f6153219-07b4-4eef-cf2b-fa7ad4e5bf01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'SELECT PLAYER_NAME \\nFROM nba_shots \\nGROUP BY PLAYER_NAME \\nORDER BY COUNT(*) DESC \\nLIMIT 1;'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result = vn.run_sql(sql)"
      ],
      "metadata": {
        "id": "rP0dka19ozF8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "U7RHfmT0o1rY",
        "outputId": "76efeb3b-9613-4ecd-c6b7-fed94c908036"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PLAYER_NAME\n",
              "0  Luka Doncic"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-09ce088c-b0c4-42ce-b384-70984c5c3805\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PLAYER_NAME</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Luka Doncic</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-09ce088c-b0c4-42ce-b384-70984c5c3805')\"\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-09ce088c-b0c4-42ce-b384-70984c5c3805 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-09ce088c-b0c4-42ce-b384-70984c5c3805');\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=\"id_3d053b6d-28d5-4727-9ba6-6c1a2d24303a\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('result')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_3d053b6d-28d5-4727-9ba6-6c1a2d24303a button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('result');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "result",
              "summary": "{\n  \"name\": \"result\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"PLAYER_NAME\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Luka Doncic\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "code = vn.generate_plotly_code(result)\n",
        "code"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "eMyjMMJCpJWZ",
        "outputId": "b433901f-e8eb-4dff-f443-74ec3d2d6d43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using model gpt-4o for 126.25 tokens (approx)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'import plotly.graph_objects as go\\n\\nfig = go.Figure(go.Indicator(\\n    mode = \"number\",\\n    value = 1,\\n    title = {\"text\": df[\\'PLAYER_NAME\\'][0]}\\n))\\n\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "figure = vn.get_plotly_figure(code, result)"
      ],
      "metadata": {
        "id": "wmnedD8Wq5KZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "figure"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "b4Vp1WSvDvJu",
        "outputId": "e2351fad-34a9-48c6-bffc-38999d36b77b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.35.2.min.js\"></script>                <div id=\"89724aa9-2e91-40f4-ab59-2c0b2813cd7f\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"89724aa9-2e91-40f4-ab59-2c0b2813cd7f\")) {                    Plotly.newPlot(                        \"89724aa9-2e91-40f4-ab59-2c0b2813cd7f\",                        [{\"mode\":\"number\",\"title\":{\"text\":\"Luka Doncic\"},\"value\":1,\"type\":\"indicator\"}],                        {\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"rgb(17,17,17)\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#f2f5fa\"},\"error_y\":{\"color\":\"#f2f5fa\"},\"marker\":{\"line\":{\"color\":\"rgb(17,17,17)\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#A2B1C6\",\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"minorgridcolor\":\"#506784\",\"startlinecolor\":\"#A2B1C6\"},\"baxis\":{\"endlinecolor\":\"#A2B1C6\",\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"minorgridcolor\":\"#506784\",\"startlinecolor\":\"#A2B1C6\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"line\":{\"color\":\"#283442\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"marker\":{\"line\":{\"color\":\"#283442\"}},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#506784\"},\"line\":{\"color\":\"rgb(17,17,17)\"}},\"header\":{\"fill\":{\"color\":\"#2a3f5f\"},\"line\":{\"color\":\"rgb(17,17,17)\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#f2f5fa\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#f2f5fa\"},\"geo\":{\"bgcolor\":\"rgb(17,17,17)\",\"lakecolor\":\"rgb(17,17,17)\",\"landcolor\":\"rgb(17,17,17)\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"#506784\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"dark\"},\"paper_bgcolor\":\"rgb(17,17,17)\",\"plot_bgcolor\":\"rgb(17,17,17)\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"bgcolor\":\"rgb(17,17,17)\",\"radialaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"},\"yaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"},\"zaxis\":{\"backgroundcolor\":\"rgb(17,17,17)\",\"gridcolor\":\"#506784\",\"gridwidth\":2,\"linecolor\":\"#506784\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#C8D4E3\"}},\"shapedefaults\":{\"line\":{\"color\":\"#f2f5fa\"}},\"sliderdefaults\":{\"bgcolor\":\"#C8D4E3\",\"bordercolor\":\"rgb(17,17,17)\",\"borderwidth\":1,\"tickwidth\":0},\"ternary\":{\"aaxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"},\"bgcolor\":\"rgb(17,17,17)\",\"caxis\":{\"gridcolor\":\"#506784\",\"linecolor\":\"#506784\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"updatemenudefaults\":{\"bgcolor\":\"#506784\",\"borderwidth\":0},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#283442\",\"linecolor\":\"#506784\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#283442\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#283442\",\"linecolor\":\"#506784\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#283442\",\"zerolinewidth\":2}}}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('89724aa9-2e91-40f4-ab59-2c0b2813cd7f');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "figure.write_image(\"plot.png\")"
      ],
      "metadata": {
        "id": "Prz4mkwJrGAZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vn.ask(\"Show me Luka Doncic's shot made percentage. Note that 1 means shot made, 0 shot miss\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qnRdF4280klx",
        "outputId": "c45e55b1-d66a-480d-8b97-077bb823f862"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Number of requested results 10 is greater than number of elements in index 4, updating n_results = 4\n",
            "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Number of requested results 10 is greater than number of elements in index 1, updating n_results = 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SQL Prompt: [{'role': 'system', 'content': \"You are a SQLite expert. Please help to generate a SQL query to answer the question. Your response should ONLY be based on the given context and follow the response guidelines and format instructions. \\n===Additional Context \\n\\nPlease refer to the following content to understand the meaning of all the columns in the source of truth. \\nSEASON_1 & SEASON_2: Season indicator variables\\nTEAM_ID: NBA's unique ID variable of that specific team in their API.\\nPLAYER_ID: NBA's unique ID variable of that specific player in their API.\\nPLAYER_NAME: Name of the player.\\nGAME_DATE: Date of the game (M-D-Y // Month-Date-Year).\\nGAME_ID: NBA's unique ID variable of that specific game in their API.\\nEVENT_TYPE: Character variable denoting a shot outcome (Made Shot // Missed Shot).\\nSHOT_MADE: True/False variable denoting a shot outcome (TRUE // FALSE).\\nACTION_TYPE: Description of shot type (layup, dunk, jump shot, etc.).\\nSHOT_TYPE: Type of shot (2PT or 3PT).\\nBASIC_ZONE: Name of the court zone the shot took place in.\\nRestricted Area, In the Paint (non-RA), Midrange, Left Corner 3, Right Corner 3, Above the Break, Backcourt.\\nZONE_NAME: Name of the side of court the shot took place in.\\nleft, left side center, center, right side center, right\\nZONE_ABB: Abbreviation of the side of court.\\n(L), (LC), (C), (RC), (R).\\nZONE_RANGE: Distance range of shot by zones.\\nLess than 8 ft., 8-16 ft. 16-24 ft. 24+ ft.\\nLOC_X: X coordinate of the shot in the x, y plane of the court (0, 50).\\nLOC_Y: Y coordinate of the shot in the x, y plane of the court (0, 50).\\nSHOT_DISTANCE: Distance of the shot with respect to the center of the hoop, in feet.\\nQUARTER: Quarter of the game.\\nMINS_LEFT: Minutes remaining in the quarter.\\nSECS_LEFT: Seconds remaining in minute of the quarter.\\n\\n\\n===Response Guidelines \\n1. If the provided context is sufficient, please generate a valid SQL query without any explanations for the question. \\n2. If the provided context is almost sufficient but requires knowledge of a specific string in a particular column, please generate an intermediate SQL query to find the distinct strings in that column. Prepend the query with a comment saying intermediate_sql \\n3. If the provided context is insufficient, please explain why it can't be generated. \\n4. Please use the most relevant table(s). \\n5. If the question has been asked and answered before, please repeat the answer exactly as it was given before. \\n6. Ensure that the output SQL is SQLite-compliant and executable, and free of syntax errors. \\n\"}, {'role': 'user', 'content': \"Show me Luka Doncic's shot made percentage.\"}, {'role': 'assistant', 'content': \"SELECT \\n    (SUM(CASE WHEN SHOT_MADE = 'TRUE' THEN 1 ELSE 0 END) * 100.0 / COUNT(*)) AS shot_made_percentage\\nFROM nba_shots\\nWHERE PLAYER_NAME = 'Luka Doncic';\"}, {'role': 'user', 'content': 'How many shots in total?'}, {'role': 'assistant', 'content': 'SELECT COUNT(*) FROM nba_shots'}, {'role': 'user', 'content': 'Who made most shots? Give me the player name'}, {'role': 'assistant', 'content': 'SELECT PLAYER_NAME \\nFROM nba_shots \\nGROUP BY PLAYER_NAME \\nORDER BY COUNT(*) DESC \\nLIMIT 1;'}, {'role': 'user', 'content': 'What are all the recorded shots in the NBA database?'}, {'role': 'assistant', 'content': 'SELECT * FROM nba_shots'}, {'role': 'user', 'content': \"Show me Luka Doncic's shot made percentage. Note that 1 means shot made, 0 shot miss\"}]\n",
            "Using model gpt-4o for 761.5 tokens (approx)\n",
            "LLM Response: SELECT \n",
            "    (SUM(SHOT_MADE) * 100.0 / COUNT(*)) AS shot_made_percentage\n",
            "FROM nba_shots\n",
            "WHERE PLAYER_NAME = 'Luka Doncic';\n",
            "Extracted SQL: SELECT \n",
            "    (SUM(SHOT_MADE) * 100.0 / COUNT(*)) AS shot_made_percentage\n",
            "FROM nba_shots\n",
            "WHERE PLAYER_NAME = 'Luka Doncic';\n",
            "SELECT \n",
            "    (SUM(SHOT_MADE) * 100.0 / COUNT(*)) AS shot_made_percentage\n",
            "FROM nba_shots\n",
            "WHERE PLAYER_NAME = 'Luka Doncic';\n",
            "   shot_made_percentage\n",
            "0             48.668281\n",
            "Using model gpt-4o for 199.25 tokens (approx)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(\"SELECT \\n    (SUM(SHOT_MADE) * 100.0 / COUNT(*)) AS shot_made_percentage\\nFROM nba_shots\\nWHERE PLAYER_NAME = 'Luka Doncic';\",\n",
              "    shot_made_percentage\n",
              " 0             48.668281,\n",
              " Figure({\n",
              "     'data': [{'mode': 'gauge+number',\n",
              "               'title': {'text': \"Luka Doncic's Shot Made Percentage\"},\n",
              "               'type': 'indicator',\n",
              "               'value': 48.6682808716707}],\n",
              "     'layout': {'template': '...'}\n",
              " }))"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "question = \"Plot the shooting percentages of the top 3 players with the highest shot made percentage. Note that 1 means shot made, 0 shot miss\""
      ],
      "metadata": {
        "id": "-ertXd_u3E-t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vn.ask(question)"
      ],
      "metadata": {
        "id": "FnKjoCL5-eZR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "52e4ab6b-eaae-4111-b34b-150b60f607f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Number of requested results 10 is greater than number of elements in index 6, updating n_results = 6\n",
            "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Number of requested results 10 is greater than number of elements in index 1, updating n_results = 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SQL Prompt: [{'role': 'system', 'content': \"You are a SQLite expert. Please help to generate a SQL query to answer the question. Your response should ONLY be based on the given context and follow the response guidelines and format instructions. \\n===Additional Context \\n\\nPlease refer to the following content to understand the meaning of all the columns in the source of truth. \\nSEASON_1 & SEASON_2: Season indicator variables\\nTEAM_ID: NBA's unique ID variable of that specific team in their API.\\nPLAYER_ID: NBA's unique ID variable of that specific player in their API.\\nPLAYER_NAME: Name of the player.\\nGAME_DATE: Date of the game (M-D-Y // Month-Date-Year).\\nGAME_ID: NBA's unique ID variable of that specific game in their API.\\nEVENT_TYPE: Character variable denoting a shot outcome (Made Shot // Missed Shot).\\nSHOT_MADE: True/False variable denoting a shot outcome (TRUE // FALSE).\\nACTION_TYPE: Description of shot type (layup, dunk, jump shot, etc.).\\nSHOT_TYPE: Type of shot (2PT or 3PT).\\nBASIC_ZONE: Name of the court zone the shot took place in.\\nRestricted Area, In the Paint (non-RA), Midrange, Left Corner 3, Right Corner 3, Above the Break, Backcourt.\\nZONE_NAME: Name of the side of court the shot took place in.\\nleft, left side center, center, right side center, right\\nZONE_ABB: Abbreviation of the side of court.\\n(L), (LC), (C), (RC), (R).\\nZONE_RANGE: Distance range of shot by zones.\\nLess than 8 ft., 8-16 ft. 16-24 ft. 24+ ft.\\nLOC_X: X coordinate of the shot in the x, y plane of the court (0, 50).\\nLOC_Y: Y coordinate of the shot in the x, y plane of the court (0, 50).\\nSHOT_DISTANCE: Distance of the shot with respect to the center of the hoop, in feet.\\nQUARTER: Quarter of the game.\\nMINS_LEFT: Minutes remaining in the quarter.\\nSECS_LEFT: Seconds remaining in minute of the quarter.\\n\\n\\n===Response Guidelines \\n1. If the provided context is sufficient, please generate a valid SQL query without any explanations for the question. \\n2. If the provided context is almost sufficient but requires knowledge of a specific string in a particular column, please generate an intermediate SQL query to find the distinct strings in that column. Prepend the query with a comment saying intermediate_sql \\n3. If the provided context is insufficient, please explain why it can't be generated. \\n4. Please use the most relevant table(s). \\n5. If the question has been asked and answered before, please repeat the answer exactly as it was given before. \\n6. Ensure that the output SQL is SQLite-compliant and executable, and free of syntax errors. \\n\"}, {'role': 'user', 'content': 'Plot the shooting percentages of the top 3 players with the highest shot made percentage.'}, {'role': 'assistant', 'content': \"WITH player_shot_stats AS (\\n    SELECT \\n        PLAYER_NAME,\\n        SUM(CASE WHEN SHOT_MADE = 'TRUE' THEN 1 ELSE 0 END) * 100.0 / COUNT(*) AS shot_made_percentage\\n    FROM nba_shots\\n    GROUP BY PLAYER_NAME\\n)\\nSELECT PLAYER_NAME, shot_made_percentage\\nFROM player_shot_stats\\nORDER BY shot_made_percentage DESC\\nLIMIT 3;\"}, {'role': 'user', 'content': \"Show me Luka Doncic's shot made percentage. Note that 1 means shot made, 0 shot miss\"}, {'role': 'assistant', 'content': \"SELECT \\n    (SUM(SHOT_MADE) * 100.0 / COUNT(*)) AS shot_made_percentage\\nFROM nba_shots\\nWHERE PLAYER_NAME = 'Luka Doncic';\"}, {'role': 'user', 'content': \"Show me Luka Doncic's shot made percentage.\"}, {'role': 'assistant', 'content': \"SELECT \\n    (SUM(CASE WHEN SHOT_MADE = 'TRUE' THEN 1 ELSE 0 END) * 100.0 / COUNT(*)) AS shot_made_percentage\\nFROM nba_shots\\nWHERE PLAYER_NAME = 'Luka Doncic';\"}, {'role': 'user', 'content': 'Who made most shots? Give me the player name'}, {'role': 'assistant', 'content': 'SELECT PLAYER_NAME \\nFROM nba_shots \\nGROUP BY PLAYER_NAME \\nORDER BY COUNT(*) DESC \\nLIMIT 1;'}, {'role': 'user', 'content': 'How many shots in total?'}, {'role': 'assistant', 'content': 'SELECT COUNT(*) FROM nba_shots'}, {'role': 'user', 'content': 'What are all the recorded shots in the NBA database?'}, {'role': 'assistant', 'content': 'SELECT * FROM nba_shots'}, {'role': 'user', 'content': 'Plot the shooting percentages of the top 3 players with the highest shot made percentage. Note that 1 means shot made, 0 shot miss'}]\n",
            "Using model gpt-4o for 925.75 tokens (approx)\n",
            "LLM Response: WITH player_shot_stats AS (\n",
            "    SELECT \n",
            "        PLAYER_NAME,\n",
            "        SUM(SHOT_MADE) * 100.0 / COUNT(*) AS shot_made_percentage\n",
            "    FROM nba_shots\n",
            "    GROUP BY PLAYER_NAME\n",
            ")\n",
            "SELECT PLAYER_NAME, shot_made_percentage\n",
            "FROM player_shot_stats\n",
            "ORDER BY shot_made_percentage DESC\n",
            "LIMIT 3;\n",
            "Extracted SQL: WITH player_shot_stats AS (\n",
            "    SELECT \n",
            "        PLAYER_NAME,\n",
            "        SUM(SHOT_MADE) * 100.0 / COUNT(*) AS shot_made_percentage\n",
            "    FROM nba_shots\n",
            "    GROUP BY PLAYER_NAME\n",
            ")\n",
            "SELECT PLAYER_NAME, shot_made_percentage\n",
            "FROM player_shot_stats\n",
            "ORDER BY shot_made_percentage DESC\n",
            "LIMIT 3;\n",
            "WITH player_shot_stats AS (\n",
            "    SELECT \n",
            "        PLAYER_NAME,\n",
            "        SUM(SHOT_MADE) * 100.0 / COUNT(*) AS shot_made_percentage\n",
            "    FROM nba_shots\n",
            "    GROUP BY PLAYER_NAME\n",
            ")\n",
            "SELECT PLAYER_NAME, shot_made_percentage\n",
            "FROM player_shot_stats\n",
            "ORDER BY shot_made_percentage DESC\n",
            "LIMIT 3;\n",
            "        PLAYER_NAME  shot_made_percentage\n",
            "0  Dereck Lively II             74.662162\n",
            "1    Charles Bassey             72.500000\n",
            "2    Daniel Gafford             72.500000\n",
            "Using model gpt-4o for 258.5 tokens (approx)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('WITH player_shot_stats AS (\\n    SELECT \\n        PLAYER_NAME,\\n        SUM(SHOT_MADE) * 100.0 / COUNT(*) AS shot_made_percentage\\n    FROM nba_shots\\n    GROUP BY PLAYER_NAME\\n)\\nSELECT PLAYER_NAME, shot_made_percentage\\nFROM player_shot_stats\\nORDER BY shot_made_percentage DESC\\nLIMIT 3;',\n",
              "         PLAYER_NAME  shot_made_percentage\n",
              " 0  Dereck Lively II             74.662162\n",
              " 1    Charles Bassey             72.500000\n",
              " 2    Daniel Gafford             72.500000,\n",
              " Figure({\n",
              "     'data': [{'alignmentgroup': 'True',\n",
              "               'hovertemplate': 'PLAYER_NAME=%{x}<br>Shot Made Percentage (%)=%{y}<extra></extra>',\n",
              "               'legendgroup': '',\n",
              "               'marker': {'color': '#636efa', 'pattern': {'shape': ''}},\n",
              "               'name': '',\n",
              "               'offsetgroup': '',\n",
              "               'orientation': 'v',\n",
              "               'showlegend': False,\n",
              "               'textposition': 'auto',\n",
              "               'type': 'bar',\n",
              "               'x': array(['Dereck Lively II', 'Charles Bassey', 'Daniel Gafford'], dtype=object),\n",
              "               'xaxis': 'x',\n",
              "               'y': array([74.66216216, 72.5       , 72.5       ]),\n",
              "               'yaxis': 'y'}],\n",
              "     'layout': {'barmode': 'relative',\n",
              "                'legend': {'tracegroupgap': 0},\n",
              "                'template': '...',\n",
              "                'title': {'text': 'Shooting Percentages of Top 3 Players'},\n",
              "                'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'title': {'text': 'PLAYER_NAME'}},\n",
              "                'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'Shot Made Percentage (%)'}}}\n",
              " }))"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "documentation = '''\n",
        "SEASON_1 & SEASON_2: Season indicator variables\n",
        "TEAM_ID: NBA's unique ID variable of that specific team in their API.\n",
        "PLAYER_ID: NBA's unique ID variable of that specific player in their API.\n",
        "PLAYER_NAME: Name of the player.\n",
        "GAME_DATE: Date of the game (M-D-Y // Month-Date-Year).\n",
        "GAME_ID: NBA's unique ID variable of that specific game in their API.\n",
        "EVENT_TYPE: Character variable denoting a shot outcome (Made Shot // Missed Shot).\n",
        "SHOT_MADE: variable denoting a shot outcome (1 // 0).\n",
        "ACTION_TYPE: Description of shot type (layup, dunk, jump shot, etc.).\n",
        "SHOT_TYPE: Type of shot (2PT or 3PT).\n",
        "BASIC_ZONE: Name of the court zone the shot took place in.\n",
        "Restricted Area, In the Paint (non-RA), Midrange, Left Corner 3, Right Corner 3, Above the Break, Backcourt.\n",
        "ZONE_NAME: Name of the side of court the shot took place in.\n",
        "left, left side center, center, right side center, right\n",
        "ZONE_ABB: Abbreviation of the side of court.\n",
        "(L), (LC), (C), (RC), (R).\n",
        "ZONE_RANGE: Distance range of shot by zones.\n",
        "Less than 8 ft., 8-16 ft. 16-24 ft. 24+ ft.\n",
        "LOC_X: X coordinate of the shot in the x, y plane of the court (0, 50).\n",
        "LOC_Y: Y coordinate of the shot in the x, y plane of the court (0, 50).\n",
        "SHOT_DISTANCE: Distance of the shot with respect to the center of the hoop, in feet.\n",
        "QUARTER: Quarter of the game.\n",
        "MINS_LEFT: Minutes remaining in the quarter.\n",
        "SECS_LEFT: Seconds remaining in minute of the quarter.\n",
        "'''"
      ],
      "metadata": {
        "id": "IsB8fH__Glj6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vn.train(documentation=f\"Please refer to the following content to understand the meaning of all the columns in the source of truth. {documentation}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "RgObiMXOGrYu",
        "outputId": "238e4d2a-b615-49d6-c5b8-107d54078e6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Adding documentation....\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'72b4017d-b35e-5e94-9314-56d7670d12ee-doc'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sql = vn.generate_sql(question)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lhuSkjrZ22PJ",
        "outputId": "862ff99f-ae31-4440-a39b-1a4bf6a2debf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Number of requested results 10 is greater than number of elements in index 7, updating n_results = 7\n",
            "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Number of requested results 10 is greater than number of elements in index 2, updating n_results = 2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SQL Prompt: [{'role': 'system', 'content': \"You are a SQLite expert. Please help to generate a SQL query to answer the question. Your response should ONLY be based on the given context and follow the response guidelines and format instructions. \\n===Additional Context \\n\\nPlease refer to the following content to understand the meaning of all the columns in the source of truth. \\nSEASON_1 & SEASON_2: Season indicator variables\\nTEAM_ID: NBA's unique ID variable of that specific team in their API.\\nPLAYER_ID: NBA's unique ID variable of that specific player in their API.\\nPLAYER_NAME: Name of the player.\\nGAME_DATE: Date of the game (M-D-Y // Month-Date-Year).\\nGAME_ID: NBA's unique ID variable of that specific game in their API.\\nEVENT_TYPE: Character variable denoting a shot outcome (Made Shot // Missed Shot).\\nSHOT_MADE: variable denoting a shot outcome (1 // 0).\\nACTION_TYPE: Description of shot type (layup, dunk, jump shot, etc.).\\nSHOT_TYPE: Type of shot (2PT or 3PT).\\nBASIC_ZONE: Name of the court zone the shot took place in.\\nRestricted Area, In the Paint (non-RA), Midrange, Left Corner 3, Right Corner 3, Above the Break, Backcourt.\\nZONE_NAME: Name of the side of court the shot took place in.\\nleft, left side center, center, right side center, right\\nZONE_ABB: Abbreviation of the side of court.\\n(L), (LC), (C), (RC), (R).\\nZONE_RANGE: Distance range of shot by zones.\\nLess than 8 ft., 8-16 ft. 16-24 ft. 24+ ft.\\nLOC_X: X coordinate of the shot in the x, y plane of the court (0, 50).\\nLOC_Y: Y coordinate of the shot in the x, y plane of the court (0, 50).\\nSHOT_DISTANCE: Distance of the shot with respect to the center of the hoop, in feet.\\nQUARTER: Quarter of the game.\\nMINS_LEFT: Minutes remaining in the quarter.\\nSECS_LEFT: Seconds remaining in minute of the quarter.\\n\\n\\nPlease refer to the following content to understand the meaning of all the columns in the source of truth. \\nSEASON_1 & SEASON_2: Season indicator variables\\nTEAM_ID: NBA's unique ID variable of that specific team in their API.\\nPLAYER_ID: NBA's unique ID variable of that specific player in their API.\\nPLAYER_NAME: Name of the player.\\nGAME_DATE: Date of the game (M-D-Y // Month-Date-Year).\\nGAME_ID: NBA's unique ID variable of that specific game in their API.\\nEVENT_TYPE: Character variable denoting a shot outcome (Made Shot // Missed Shot).\\nSHOT_MADE: True/False variable denoting a shot outcome (TRUE // FALSE).\\nACTION_TYPE: Description of shot type (layup, dunk, jump shot, etc.).\\nSHOT_TYPE: Type of shot (2PT or 3PT).\\nBASIC_ZONE: Name of the court zone the shot took place in.\\nRestricted Area, In the Paint (non-RA), Midrange, Left Corner 3, Right Corner 3, Above the Break, Backcourt.\\nZONE_NAME: Name of the side of court the shot took place in.\\nleft, left side center, center, right side center, right\\nZONE_ABB: Abbreviation of the side of court.\\n(L), (LC), (C), (RC), (R).\\nZONE_RANGE: Distance range of shot by zones.\\nLess than 8 ft., 8-16 ft. 16-24 ft. 24+ ft.\\nLOC_X: X coordinate of the shot in the x, y plane of the court (0, 50).\\nLOC_Y: Y coordinate of the shot in the x, y plane of the court (0, 50).\\nSHOT_DISTANCE: Distance of the shot with respect to the center of the hoop, in feet.\\nQUARTER: Quarter of the game.\\nMINS_LEFT: Minutes remaining in the quarter.\\nSECS_LEFT: Seconds remaining in minute of the quarter.\\n\\n\\n===Response Guidelines \\n1. If the provided context is sufficient, please generate a valid SQL query without any explanations for the question. \\n2. If the provided context is almost sufficient but requires knowledge of a specific string in a particular column, please generate an intermediate SQL query to find the distinct strings in that column. Prepend the query with a comment saying intermediate_sql \\n3. If the provided context is insufficient, please explain why it can't be generated. \\n4. Please use the most relevant table(s). \\n5. If the question has been asked and answered before, please repeat the answer exactly as it was given before. \\n6. Ensure that the output SQL is SQLite-compliant and executable, and free of syntax errors. \\n\"}, {'role': 'user', 'content': 'Plot the shooting percentages of the top 3 players with the highest shot made percentage. Note that 1 means shot made, 0 shot miss'}, {'role': 'assistant', 'content': 'WITH player_shot_stats AS (\\n    SELECT \\n        PLAYER_NAME,\\n        SUM(SHOT_MADE) * 100.0 / COUNT(*) AS shot_made_percentage\\n    FROM nba_shots\\n    GROUP BY PLAYER_NAME\\n)\\nSELECT PLAYER_NAME, shot_made_percentage\\nFROM player_shot_stats\\nORDER BY shot_made_percentage DESC\\nLIMIT 3;'}, {'role': 'user', 'content': 'Plot the shooting percentages of the top 3 players with the highest shot made percentage.'}, {'role': 'assistant', 'content': \"WITH player_shot_stats AS (\\n    SELECT \\n        PLAYER_NAME,\\n        SUM(CASE WHEN SHOT_MADE = 'TRUE' THEN 1 ELSE 0 END) * 100.0 / COUNT(*) AS shot_made_percentage\\n    FROM nba_shots\\n    GROUP BY PLAYER_NAME\\n)\\nSELECT PLAYER_NAME, shot_made_percentage\\nFROM player_shot_stats\\nORDER BY shot_made_percentage DESC\\nLIMIT 3;\"}, {'role': 'user', 'content': \"Show me Luka Doncic's shot made percentage. Note that 1 means shot made, 0 shot miss\"}, {'role': 'assistant', 'content': \"SELECT \\n    (SUM(SHOT_MADE) * 100.0 / COUNT(*)) AS shot_made_percentage\\nFROM nba_shots\\nWHERE PLAYER_NAME = 'Luka Doncic';\"}, {'role': 'user', 'content': \"Show me Luka Doncic's shot made percentage.\"}, {'role': 'assistant', 'content': \"SELECT \\n    (SUM(CASE WHEN SHOT_MADE = 'TRUE' THEN 1 ELSE 0 END) * 100.0 / COUNT(*)) AS shot_made_percentage\\nFROM nba_shots\\nWHERE PLAYER_NAME = 'Luka Doncic';\"}, {'role': 'user', 'content': 'Who made most shots? Give me the player name'}, {'role': 'assistant', 'content': 'SELECT PLAYER_NAME \\nFROM nba_shots \\nGROUP BY PLAYER_NAME \\nORDER BY COUNT(*) DESC \\nLIMIT 1;'}, {'role': 'user', 'content': 'How many shots in total?'}, {'role': 'assistant', 'content': 'SELECT COUNT(*) FROM nba_shots'}, {'role': 'user', 'content': 'What are all the recorded shots in the NBA database?'}, {'role': 'assistant', 'content': 'SELECT * FROM nba_shots'}, {'role': 'user', 'content': 'Plot the shooting percentages of the top 3 players with the highest shot made percentage. Note that 1 means shot made, 0 shot miss'}]\n",
            "Using model gpt-4o for 1406.25 tokens (approx)\n",
            "LLM Response: WITH player_shot_stats AS (\n",
            "    SELECT \n",
            "        PLAYER_NAME,\n",
            "        SUM(SHOT_MADE) * 100.0 / COUNT(*) AS shot_made_percentage\n",
            "    FROM nba_shots\n",
            "    GROUP BY PLAYER_NAME\n",
            ")\n",
            "SELECT PLAYER_NAME, shot_made_percentage\n",
            "FROM player_shot_stats\n",
            "ORDER BY shot_made_percentage DESC\n",
            "LIMIT 3;\n",
            "Extracted SQL: WITH player_shot_stats AS (\n",
            "    SELECT \n",
            "        PLAYER_NAME,\n",
            "        SUM(SHOT_MADE) * 100.0 / COUNT(*) AS shot_made_percentage\n",
            "    FROM nba_shots\n",
            "    GROUP BY PLAYER_NAME\n",
            ")\n",
            "SELECT PLAYER_NAME, shot_made_percentage\n",
            "FROM player_shot_stats\n",
            "ORDER BY shot_made_percentage DESC\n",
            "LIMIT 3;\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result = vn.run_sql(sql)"
      ],
      "metadata": {
        "id": "tJoFT_rz3NhJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(result))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OZjv6ONC3Qds",
        "outputId": "09de2e8c-1608-4287-d09b-0510be440048"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EqJ8N07L4FsS",
        "outputId": "ebd145b1-898e-459f-fe75-6d57623aea3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        PLAYER_NAME  shot_made_percentage\n",
            "0  Dereck Lively II             74.662162\n",
            "1    Charles Bassey             72.500000\n",
            "2    Daniel Gafford             72.500000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "figure = px.bar(result, x='PLAYER_NAME', y='shot_made_percentage', title='Top Shooters')\n",
        "\n",
        "figure.update_layout(\n",
        "    xaxis_title=\"Player Name\",\n",
        "    yaxis_title=\"Shot Made %\",\n",
        "    bargap=0.2,  # gap between bars\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "LbJiZQUY3hOS",
        "outputId": "894fb031-f0c9-4c3f-84cc-e014903e35d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.35.2.min.js\"></script>                <div id=\"c3eb11fd-a9c8-4703-89e8-65b2211204fa\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"c3eb11fd-a9c8-4703-89e8-65b2211204fa\")) {                    Plotly.newPlot(                        \"c3eb11fd-a9c8-4703-89e8-65b2211204fa\",                        [{\"alignmentgroup\":\"True\",\"hovertemplate\":\"PLAYER_NAME=%{x}\\u003cbr\\u003eshot_made_percentage=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"\",\"offsetgroup\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"textposition\":\"auto\",\"x\":[\"Dereck Lively II\",\"Charles Bassey\",\"Daniel Gafford\"],\"xaxis\":\"x\",\"y\":[74.66216216216216,72.5,72.5],\"yaxis\":\"y\",\"type\":\"bar\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Player Name\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Shot Made %\"}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Top Shooters\"},\"barmode\":\"relative\",\"bargap\":0.2},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('c3eb11fd-a9c8-4703-89e8-65b2211204fa');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vn.ask(\"Every 5 feet as a range, plot the total shots per shot distance\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "4LK9VLwB5Lzn",
        "outputId": "bb90503b-4a99-49fd-9c85-f5b1a1d8090b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Number of requested results 10 is greater than number of elements in index 7, updating n_results = 7\n",
            "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Number of requested results 10 is greater than number of elements in index 2, updating n_results = 2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SQL Prompt: [{'role': 'system', 'content': \"You are a SQLite expert. Please help to generate a SQL query to answer the question. Your response should ONLY be based on the given context and follow the response guidelines and format instructions. \\n===Additional Context \\n\\nPlease refer to the following content to understand the meaning of all the columns in the source of truth. \\nSEASON_1 & SEASON_2: Season indicator variables\\nTEAM_ID: NBA's unique ID variable of that specific team in their API.\\nPLAYER_ID: NBA's unique ID variable of that specific player in their API.\\nPLAYER_NAME: Name of the player.\\nGAME_DATE: Date of the game (M-D-Y // Month-Date-Year).\\nGAME_ID: NBA's unique ID variable of that specific game in their API.\\nEVENT_TYPE: Character variable denoting a shot outcome (Made Shot // Missed Shot).\\nSHOT_MADE: variable denoting a shot outcome (1 // 0).\\nACTION_TYPE: Description of shot type (layup, dunk, jump shot, etc.).\\nSHOT_TYPE: Type of shot (2PT or 3PT).\\nBASIC_ZONE: Name of the court zone the shot took place in.\\nRestricted Area, In the Paint (non-RA), Midrange, Left Corner 3, Right Corner 3, Above the Break, Backcourt.\\nZONE_NAME: Name of the side of court the shot took place in.\\nleft, left side center, center, right side center, right\\nZONE_ABB: Abbreviation of the side of court.\\n(L), (LC), (C), (RC), (R).\\nZONE_RANGE: Distance range of shot by zones.\\nLess than 8 ft., 8-16 ft. 16-24 ft. 24+ ft.\\nLOC_X: X coordinate of the shot in the x, y plane of the court (0, 50).\\nLOC_Y: Y coordinate of the shot in the x, y plane of the court (0, 50).\\nSHOT_DISTANCE: Distance of the shot with respect to the center of the hoop, in feet.\\nQUARTER: Quarter of the game.\\nMINS_LEFT: Minutes remaining in the quarter.\\nSECS_LEFT: Seconds remaining in minute of the quarter.\\n\\n\\nPlease refer to the following content to understand the meaning of all the columns in the source of truth. \\nSEASON_1 & SEASON_2: Season indicator variables\\nTEAM_ID: NBA's unique ID variable of that specific team in their API.\\nPLAYER_ID: NBA's unique ID variable of that specific player in their API.\\nPLAYER_NAME: Name of the player.\\nGAME_DATE: Date of the game (M-D-Y // Month-Date-Year).\\nGAME_ID: NBA's unique ID variable of that specific game in their API.\\nEVENT_TYPE: Character variable denoting a shot outcome (Made Shot // Missed Shot).\\nSHOT_MADE: True/False variable denoting a shot outcome (TRUE // FALSE).\\nACTION_TYPE: Description of shot type (layup, dunk, jump shot, etc.).\\nSHOT_TYPE: Type of shot (2PT or 3PT).\\nBASIC_ZONE: Name of the court zone the shot took place in.\\nRestricted Area, In the Paint (non-RA), Midrange, Left Corner 3, Right Corner 3, Above the Break, Backcourt.\\nZONE_NAME: Name of the side of court the shot took place in.\\nleft, left side center, center, right side center, right\\nZONE_ABB: Abbreviation of the side of court.\\n(L), (LC), (C), (RC), (R).\\nZONE_RANGE: Distance range of shot by zones.\\nLess than 8 ft., 8-16 ft. 16-24 ft. 24+ ft.\\nLOC_X: X coordinate of the shot in the x, y plane of the court (0, 50).\\nLOC_Y: Y coordinate of the shot in the x, y plane of the court (0, 50).\\nSHOT_DISTANCE: Distance of the shot with respect to the center of the hoop, in feet.\\nQUARTER: Quarter of the game.\\nMINS_LEFT: Minutes remaining in the quarter.\\nSECS_LEFT: Seconds remaining in minute of the quarter.\\n\\n\\n===Response Guidelines \\n1. If the provided context is sufficient, please generate a valid SQL query without any explanations for the question. \\n2. If the provided context is almost sufficient but requires knowledge of a specific string in a particular column, please generate an intermediate SQL query to find the distinct strings in that column. Prepend the query with a comment saying intermediate_sql \\n3. If the provided context is insufficient, please explain why it can't be generated. \\n4. Please use the most relevant table(s). \\n5. If the question has been asked and answered before, please repeat the answer exactly as it was given before. \\n6. Ensure that the output SQL is SQLite-compliant and executable, and free of syntax errors. \\n\"}, {'role': 'user', 'content': 'Plot the shooting percentages of the top 3 players with the highest shot made percentage. Note that 1 means shot made, 0 shot miss'}, {'role': 'assistant', 'content': 'WITH player_shot_stats AS (\\n    SELECT \\n        PLAYER_NAME,\\n        SUM(SHOT_MADE) * 100.0 / COUNT(*) AS shot_made_percentage\\n    FROM nba_shots\\n    GROUP BY PLAYER_NAME\\n)\\nSELECT PLAYER_NAME, shot_made_percentage\\nFROM player_shot_stats\\nORDER BY shot_made_percentage DESC\\nLIMIT 3;'}, {'role': 'user', 'content': 'Plot the shooting percentages of the top 3 players with the highest shot made percentage.'}, {'role': 'assistant', 'content': \"WITH player_shot_stats AS (\\n    SELECT \\n        PLAYER_NAME,\\n        SUM(CASE WHEN SHOT_MADE = 'TRUE' THEN 1 ELSE 0 END) * 100.0 / COUNT(*) AS shot_made_percentage\\n    FROM nba_shots\\n    GROUP BY PLAYER_NAME\\n)\\nSELECT PLAYER_NAME, shot_made_percentage\\nFROM player_shot_stats\\nORDER BY shot_made_percentage DESC\\nLIMIT 3;\"}, {'role': 'user', 'content': \"Show me Luka Doncic's shot made percentage. Note that 1 means shot made, 0 shot miss\"}, {'role': 'assistant', 'content': \"SELECT \\n    (SUM(SHOT_MADE) * 100.0 / COUNT(*)) AS shot_made_percentage\\nFROM nba_shots\\nWHERE PLAYER_NAME = 'Luka Doncic';\"}, {'role': 'user', 'content': 'How many shots in total?'}, {'role': 'assistant', 'content': 'SELECT COUNT(*) FROM nba_shots'}, {'role': 'user', 'content': \"Show me Luka Doncic's shot made percentage.\"}, {'role': 'assistant', 'content': \"SELECT \\n    (SUM(CASE WHEN SHOT_MADE = 'TRUE' THEN 1 ELSE 0 END) * 100.0 / COUNT(*)) AS shot_made_percentage\\nFROM nba_shots\\nWHERE PLAYER_NAME = 'Luka Doncic';\"}, {'role': 'user', 'content': 'Who made most shots? Give me the player name'}, {'role': 'assistant', 'content': 'SELECT PLAYER_NAME \\nFROM nba_shots \\nGROUP BY PLAYER_NAME \\nORDER BY COUNT(*) DESC \\nLIMIT 1;'}, {'role': 'user', 'content': 'What are all the recorded shots in the NBA database?'}, {'role': 'assistant', 'content': 'SELECT * FROM nba_shots'}, {'role': 'user', 'content': 'Every 5 feet as a range, plot the total shots per shot distance'}]\n",
            "Using model gpt-4o for 1389.5 tokens (approx)\n",
            "LLM Response: ```sql\n",
            "SELECT \n",
            "    (SHOT_DISTANCE / 5) * 5 AS distance_range_start,\n",
            "    ((SHOT_DISTANCE / 5) * 5) + 4 AS distance_range_end,\n",
            "    COUNT(*) AS total_shots\n",
            "FROM nba_shots\n",
            "GROUP BY distance_range_start, distance_range_end\n",
            "ORDER BY distance_range_start;\n",
            "```\n",
            "Extracted SQL: SELECT \n",
            "    (SHOT_DISTANCE / 5) * 5 AS distance_range_start,\n",
            "    ((SHOT_DISTANCE / 5) * 5) + 4 AS distance_range_end,\n",
            "    COUNT(*) AS total_shots\n",
            "FROM nba_shots\n",
            "GROUP BY distance_range_start, distance_range_end\n",
            "ORDER BY distance_range_start;\n",
            "SELECT \n",
            "    (SHOT_DISTANCE / 5) * 5 AS distance_range_start,\n",
            "    ((SHOT_DISTANCE / 5) * 5) + 4 AS distance_range_end,\n",
            "    COUNT(*) AS total_shots\n",
            "FROM nba_shots\n",
            "GROUP BY distance_range_start, distance_range_end\n",
            "ORDER BY distance_range_start;\n",
            "    distance_range_start  distance_range_end  total_shots\n",
            "0                      0                   4        72835\n",
            "1                      5                   9        25984\n",
            "2                     10                  14        17941\n",
            "3                     15                  19        12648\n",
            "4                     20                  24        33387\n",
            "5                     25                  29        53878\n",
            "6                     30                  34         1377\n",
            "7                     35                  39          152\n",
            "8                     40                  44          101\n",
            "9                     45                  49          118\n",
            "10                    50                  54           91\n",
            "11                    55                  59           71\n",
            "12                    60                  64           47\n",
            "13                    65                  69           35\n",
            "14                    70                  74           17\n",
            "15                    75                  79           14\n",
            "16                    80                  84            5\n",
            "Using model gpt-4o for 238.5 tokens (approx)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('SELECT \\n    (SHOT_DISTANCE / 5) * 5 AS distance_range_start,\\n    ((SHOT_DISTANCE / 5) * 5) + 4 AS distance_range_end,\\n    COUNT(*) AS total_shots\\nFROM nba_shots\\nGROUP BY distance_range_start, distance_range_end\\nORDER BY distance_range_start;',\n",
              "     distance_range_start  distance_range_end  total_shots distance_range\n",
              " 0                      0                   4        72835       0-4 feet\n",
              " 1                      5                   9        25984       5-9 feet\n",
              " 2                     10                  14        17941     10-14 feet\n",
              " 3                     15                  19        12648     15-19 feet\n",
              " 4                     20                  24        33387     20-24 feet\n",
              " 5                     25                  29        53878     25-29 feet\n",
              " 6                     30                  34         1377     30-34 feet\n",
              " 7                     35                  39          152     35-39 feet\n",
              " 8                     40                  44          101     40-44 feet\n",
              " 9                     45                  49          118     45-49 feet\n",
              " 10                    50                  54           91     50-54 feet\n",
              " 11                    55                  59           71     55-59 feet\n",
              " 12                    60                  64           47     60-64 feet\n",
              " 13                    65                  69           35     65-69 feet\n",
              " 14                    70                  74           17     70-74 feet\n",
              " 15                    75                  79           14     75-79 feet\n",
              " 16                    80                  84            5     80-84 feet,\n",
              " Figure({\n",
              "     'data': [{'alignmentgroup': 'True',\n",
              "               'hovertemplate': 'Shot Distance Range=%{x}<br>Total Shots=%{y}<extra></extra>',\n",
              "               'legendgroup': '',\n",
              "               'marker': {'color': '#636efa', 'pattern': {'shape': ''}},\n",
              "               'name': '',\n",
              "               'offsetgroup': '',\n",
              "               'orientation': 'v',\n",
              "               'showlegend': False,\n",
              "               'textposition': 'auto',\n",
              "               'type': 'bar',\n",
              "               'x': array(['0-4 feet', '5-9 feet', '10-14 feet', '15-19 feet', '20-24 feet',\n",
              "                           '25-29 feet', '30-34 feet', '35-39 feet', '40-44 feet', '45-49 feet',\n",
              "                           '50-54 feet', '55-59 feet', '60-64 feet', '65-69 feet', '70-74 feet',\n",
              "                           '75-79 feet', '80-84 feet'], dtype=object),\n",
              "               'xaxis': 'x',\n",
              "               'y': array([72835, 25984, 17941, 12648, 33387, 53878,  1377,   152,   101,   118,\n",
              "                              91,    71,    47,    35,    17,    14,     5]),\n",
              "               'yaxis': 'y'}],\n",
              "     'layout': {'barmode': 'relative',\n",
              "                'legend': {'tracegroupgap': 0},\n",
              "                'template': '...',\n",
              "                'title': {'text': 'Total Shots Per Shot Distance Range'},\n",
              "                'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'title': {'text': 'Shot Distance Range'}},\n",
              "                'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'Total Shots'}}}\n",
              " }))"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    }
  ],
  "metadata": {
    "language_info": {
      "name": "python"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}