Question_Answering_over_SQL_Data_Using_LangGraph_Framework.ipynb

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    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Detailed Article Explaination\n",
        "\n",
        "The detailed code explanation for this article is available at the following link:\n",
        "\n",
        "https://www.daniweb.com/programming/computer-science/tutorials/542711/question-answering-over-sql-data-using-langgraph-framework\n",
        "\n",
        "For my other articles for Daniweb.com, please see this link:\n",
        "\n",
        "https://www.daniweb.com/members/1235222/usmanmalik57"
      ],
      "metadata": {
        "id": "tDGRz-q1J4K3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing and Installing Required Libraries"
      ],
      "metadata": {
        "id": "wfO5cysbKfFr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain-community\n",
        "!pip install langchain-openai\n",
        "!pip install langgraph"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lIjFIv-8L7c2",
        "outputId": "06663a06-fa0d-497c-ebc7-525b4d937786"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hInstalling collected packages: tiktoken, langchain-openai\n",
            "Successfully installed langchain-openai-0.2.10 tiktoken-0.8.0\n",
            "Collecting langgraph\n",
            "  Downloading langgraph-0.2.53-py3-none-any.whl.metadata (15 kB)\n",
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            "Installing collected packages: langgraph-sdk, langgraph-checkpoint, langgraph\n",
            "Successfully installed langgraph-0.2.53 langgraph-checkpoint-2.0.7 langgraph-sdk-0.1.40\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sqlite3\n",
        "import pandas as pd\n",
        "import csv\n",
        "import os\n",
        "\n",
        "from langchain_community.utilities import SQLDatabase\n",
        "from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool\n",
        "from langchain_openai import ChatOpenAI\n",
        "from langchain import hub\n",
        "from langgraph.graph import START, StateGraph\n",
        "\n",
        "from typing_extensions import Annotated\n",
        "from IPython.display import Image, display\n",
        "\n",
        "from google.colab import userdata\n",
        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY')"
      ],
      "metadata": {
        "id": "mltry-GAL9TO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating a Dummy SQLite Database"
      ],
      "metadata": {
        "id": "0BVgatvqKDpu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('Titanic-Dataset.csv')\n",
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "56LHb7g2O_NB",
        "outputId": "92801495-1043-4163-9636-5dce7128adbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "\n",
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              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
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              "0      0         A/5 21171   7.2500   NaN        S  \n",
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              "3      0            113803  53.1000  C123        S  \n",
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              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-50ca68d0-6ae6-45a6-92d4-7fdf32610132\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-50ca68d0-6ae6-45a6-92d4-7fdf32610132')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-50ca68d0-6ae6-45a6-92d4-7fdf32610132 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataset",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334044,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.693428597180905,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import sqlite3\n",
        "\n",
        "def csv_to_sqlite(csv_file_path, database_name):\n",
        "    \"\"\"\n",
        "    Converts a CSV file into an SQLite database with proper column names from the CSV headers.\n",
        "\n",
        "    Parameters:\n",
        "        csv_file_path (str): Path to the CSV file.\n",
        "        database_name (str): Name of the SQLite database to create (e.g., 'output.db').\n",
        "\n",
        "    Returns:\n",
        "        str: Message indicating success or failure.\n",
        "    \"\"\"\n",
        "    try:\n",
        "        # Read the CSV file into a pandas DataFrame with headers\n",
        "        df = pd.read_csv(csv_file_path)\n",
        "\n",
        "        # Connect to SQLite database (or create it if it doesn't exist)\n",
        "        conn = sqlite3.connect(database_name)\n",
        "\n",
        "        # Use the CSV file name (without extension) as the table name\n",
        "        table_name = csv_file_path.split('/')[-1].split('.')[0]\n",
        "\n",
        "        # Write the DataFrame to the SQLite database with headers as column names\n",
        "        df.to_sql(table_name, conn, if_exists='replace', index=False)\n",
        "\n",
        "        # Close the connection\n",
        "        conn.close()\n",
        "\n",
        "        return f\"Data from '{csv_file_path}' has been successfully stored in '{database_name}', table '{table_name}'.\"\n",
        "\n",
        "    except Exception as e:\n",
        "        return f\"An error occurred: {e}\"\n",
        "\n",
        "# Example usage\n",
        "csv_file = 'Titanic-Dataset.csv'  # Replace with your CSV file path\n",
        "database = 'titanic.db'  # Replace with your desired SQLite database name\n",
        "result = csv_to_sqlite(csv_file, database)\n",
        "print(result)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kDPh38jOKlyi",
        "outputId": "819306de-5cdf-47d8-d152-a937acca51df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data from 'Titanic-Dataset.csv' has been successfully stored in 'titanic.db', table 'Titanic-Dataset'.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "db = SQLDatabase.from_uri(\"sqlite:////content/titanic.db\")\n",
        "print(db.dialect)\n",
        "print(db.get_table_info())\n",
        "print(db.get_usable_table_names())\n",
        "db.run(\"SELECT * FROM 'Titanic-Dataset' LIMIT 10;\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "jGxoUD0xPRsm",
        "outputId": "995e4dab-8fad-417c-f14a-8745932f1be7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sqlite\n",
            "\n",
            "CREATE TABLE \"Titanic-Dataset\" (\n",
            "\t\"PassengerId\" INTEGER, \n",
            "\t\"Survived\" INTEGER, \n",
            "\t\"Pclass\" INTEGER, \n",
            "\t\"Name\" TEXT, \n",
            "\t\"Sex\" TEXT, \n",
            "\t\"Age\" REAL, \n",
            "\t\"SibSp\" INTEGER, \n",
            "\t\"Parch\" INTEGER, \n",
            "\t\"Ticket\" TEXT, \n",
            "\t\"Fare\" REAL, \n",
            "\t\"Cabin\" TEXT, \n",
            "\t\"Embarked\" TEXT\n",
            ")\n",
            "\n",
            "/*\n",
            "3 rows from Titanic-Dataset table:\n",
            "PassengerId\tSurvived\tPclass\tName\tSex\tAge\tSibSp\tParch\tTicket\tFare\tCabin\tEmbarked\n",
            "1\t0\t3\tBraund, Mr. Owen Harris\tmale\t22.0\t1\t0\tA/5 21171\t7.25\tNone\tS\n",
            "2\t1\t1\tCumings, Mrs. John Bradley (Florence Briggs Thayer)\tfemale\t38.0\t1\t0\tPC 17599\t71.2833\tC85\tC\n",
            "3\t1\t3\tHeikkinen, Miss. Laina\tfemale\t26.0\t0\t0\tSTON/O2. 3101282\t7.925\tNone\tS\n",
            "*/\n",
            "['Titanic-Dataset']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"[(1, 0, 3, 'Braund, Mr. Owen Harris', 'male', 22.0, 1, 0, 'A/5 21171', 7.25, None, 'S'), (2, 1, 1, 'Cumings, Mrs. John Bradley (Florence Briggs Thayer)', 'female', 38.0, 1, 0, 'PC 17599', 71.2833, 'C85', 'C'), (3, 1, 3, 'Heikkinen, Miss. Laina', 'female', 26.0, 0, 0, 'STON/O2. 3101282', 7.925, None, 'S'), (4, 1, 1, 'Futrelle, Mrs. Jacques Heath (Lily May Peel)', 'female', 35.0, 1, 0, '113803', 53.1, 'C123', 'S'), (5, 0, 3, 'Allen, Mr. William Henry', 'male', 35.0, 0, 0, '373450', 8.05, None, 'S'), (6, 0, 3, 'Moran, Mr. James', 'male', None, 0, 0, '330877', 8.4583, None, 'Q'), (7, 0, 1, 'McCarthy, Mr. Timothy J', 'male', 54.0, 0, 0, '17463', 51.8625, 'E46', 'S'), (8, 0, 3, 'Palsson, Master. Gosta Leonard', 'male', 2.0, 3, 1, '349909', 21.075, None, 'S'), (9, 1, 3, 'Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)', 'female', 27.0, 0, 2, '347742', 11.1333, None, 'S'), (10, 1, 2, 'Nasser, Mrs. Nicholas (Adele Achem)', 'female', 14.0, 1, 0, '237736', 30.0708, None, 'C')]\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Developing a LangGraph Agent for Question/Answering Over SQL Data"
      ],
      "metadata": {
        "id": "9Yabl82aNc4p"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Defining the Agent State and LLM"
      ],
      "metadata": {
        "id": "M28-TgPaU_WD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from typing_extensions import TypedDict\n",
        "\n",
        "\n",
        "class State(TypedDict):\n",
        "    question: str\n",
        "    query: str\n",
        "    result: str\n",
        "    answer: str"
      ],
      "metadata": {
        "id": "EHTx3dfvPOkk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llm = ChatOpenAI(model=\"gpt-4o\")"
      ],
      "metadata": {
        "id": "VmeUX3stPPCO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Convert Natural Languge Questions to SQL Query"
      ],
      "metadata": {
        "id": "0CIsn-G8VDlN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain import hub\n",
        "\n",
        "query_prompt_template = hub.pull(\"langchain-ai/sql-query-system-prompt\")\n",
        "\n",
        "assert len(query_prompt_template.messages) == 1\n",
        "query_prompt_template.messages[0].pretty_print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c-ybRAjyVJ7E",
        "outputId": "5e52ad9f-4ac3-43d4-e10d-9ba157d983ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/langsmith/client.py:241: LangSmithMissingAPIKeyWarning: API key must be provided when using hosted LangSmith API\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================================\u001b[1m System Message \u001b[0m================================\n",
            "\n",
            "Given an input question, create a syntactically correct \u001b[33;1m\u001b[1;3m{dialect}\u001b[0m query to run to help find the answer. Unless the user specifies in his question a specific number of examples they wish to obtain, always limit your query to at most \u001b[33;1m\u001b[1;3m{top_k}\u001b[0m results. You can order the results by a relevant column to return the most interesting examples in the database.\n",
            "\n",
            "Never query for all the columns from a specific table, only ask for a the few relevant columns given the question.\n",
            "\n",
            "Pay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.\n",
            "\n",
            "Only use the following tables:\n",
            "\u001b[33;1m\u001b[1;3m{table_info}\u001b[0m\n",
            "\n",
            "Question: \u001b[33;1m\u001b[1;3m{input}\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class QueryOutput(TypedDict):\n",
        "    \"\"\"Generated SQL query.\"\"\"\n",
        "\n",
        "    query: Annotated[str, ..., \"Syntactically valid SQL query.\"]\n",
        "\n",
        "\n",
        "def convert_question_to_query(state: State):\n",
        "    \"\"\"Generate SQL query to fetch information.\"\"\"\n",
        "    prompt = query_prompt_template.invoke(\n",
        "        {\n",
        "            \"dialect\": db.dialect,\n",
        "            \"top_k\": 10,\n",
        "            \"table_info\": db.get_table_info(),\n",
        "            \"input\": state[\"question\"],\n",
        "        }\n",
        "    )\n",
        "    structured_llm = llm.with_structured_output(QueryOutput)\n",
        "    result = structured_llm.invoke(prompt)\n",
        "    return {\"query\": result[\"query\"]}"
      ],
      "metadata": {
        "id": "R2I4ReWRVKmk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "question = {\"question\": \"How many male passengers are there in the dataset\"}\n",
        "query = convert_question_to_query(question)\n",
        "print(query)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A5blQVO6VsLL",
        "outputId": "9c6af7e4-ebcb-43ca-908f-f1ab1455f7f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'query': 'SELECT COUNT(PassengerId) FROM \"Titanic-Dataset\" WHERE Sex = \\'male\\';'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Execute SQL Queries"
      ],
      "metadata": {
        "id": "KRoMF_j_VxtP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def execute_query(state: State):\n",
        "    \"\"\"Execute SQL query.\"\"\"\n",
        "    execute_query_tool = QuerySQLDataBaseTool(db=db)\n",
        "    return {\"result\": execute_query_tool.invoke(state[\"query\"])}\n",
        "\n",
        "result = execute_query(query)\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9KazlkFdXFZp",
        "outputId": "d87625ad-f354-4af7-9cff-7f2a3271d192"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'result': '[(577,)]'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"Sex\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "67f5N9B2XNOU",
        "outputId": "5b2b1735-5a9b-4ce7-cd19-d6baa547d0d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sex\n",
              "male      577\n",
              "female    314\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>577</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>314</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generate Final Response"
      ],
      "metadata": {
        "id": "F1_ZlhcAXx66"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_answer(state: State):\n",
        "    \"\"\"Answer question using retrieved information as context.\"\"\"\n",
        "    prompt = (\n",
        "        \"Given the following user question, corresponding SQL query, \"\n",
        "        \"and SQL result, answer the user question.\\n\\n\"\n",
        "        f'Question: {state[\"question\"]}\\n'\n",
        "        f'SQL Query: {state[\"query\"]}\\n'\n",
        "        f'SQL Result: {state[\"result\"]}'\n",
        "    )\n",
        "    response = llm.invoke(prompt)\n",
        "    return {\"answer\": response.content}"
      ],
      "metadata": {
        "id": "Lc2M5fkMX8Ay"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Adding Nodes to LangGraph and Generating Response"
      ],
      "metadata": {
        "id": "3NLGbNasYESq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "graph_builder = StateGraph(State).add_sequence(\n",
        "    [convert_question_to_query, execute_query, generate_answer]\n",
        ")\n",
        "graph_builder.add_edge(START, \"convert_question_to_query\")\n",
        "graph = graph_builder.compile()\n",
        "\n",
        "display(Image(graph.get_graph().draw_mermaid_png()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "UW3Cm9fxZDdw",
        "outputId": "0f72d5f0-75c4-4b1e-b925-5d997b3c0b30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_stream_response(question):\n",
        "  for step in graph.stream(question, stream_mode=\"updates\"):\n",
        "    print(step)"
      ],
      "metadata": {
        "id": "9t2wYcW9Z1Ju"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "question = {\"question\": \"How many male passengers are there in the dataset\"}\n",
        "generate_stream_response(question)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qlL5DdbCZDye",
        "outputId": "10ea5a21-7f9b-4a24-e573-69ace9ce7988"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'convert_question_to_query': {'query': 'SELECT COUNT(PassengerId) AS MalePassengerCount FROM \"Titanic-Dataset\" WHERE Sex = \\'male\\';'}}\n",
            "{'execute_query': {'result': '[(577,)]'}}\n",
            "{'generate_answer': {'answer': 'There are 577 male passengers in the dataset.'}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "question = {\"question\": \"What is the average age of male passengers?\"}\n",
        "generate_stream_response(question)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4hgAhYyxZQ4O",
        "outputId": "deaba763-d80f-4704-e279-c5edea6ae76f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'convert_question_to_query': {'query': 'SELECT AVG(Age) AS AverageAge FROM \"Titanic-Dataset\" WHERE Sex = \\'male\\''}}\n",
            "{'execute_query': {'result': '[(30.72664459161148,)]'}}\n",
            "{'generate_answer': {'answer': 'The average age of male passengers is approximately 30.73 years.'}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_answer = graph.invoke(question)\n",
        "print(final_answer['answer'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DEPQLZRvZqg7",
        "outputId": "4c56c70e-8c0f-4435-89e8-185af9add88a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The average age of male passengers is approximately 30.73 years.\n"
          ]
        }
      ]
    }
  ]
}