database_sql_agent.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1nilp8ZWLFxE"
      },
      "outputs": [],
      "source": [
        "%pip install --upgrade --quiet pysqlite3 openai langchain langchain-community langchain-experimental"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#-------------\n",
        "#import libraries\n",
        "#-------------\n",
        "\n",
        "from langchain_community.utilities.sql_database import SQLDatabase\n",
        "import sqlite3\n",
        "import pandas as pd\n",
        "import os\n",
        "from langchain.llms import OpenAI\n",
        "from langchain.utilities import SQLDatabase\n",
        "from langchain_experimental.sql import SQLDatabaseChain\n",
        "\n",
        "\n",
        "#-------------\n",
        "# Connect to quantum database\n",
        "#-------------\n",
        "\n",
        "conn = sqlite3.connect('quantum.db')\n",
        "cursor = conn.cursor()\n",
        "\n",
        "#-------------\n",
        "#Create table 1\n",
        "#-------------\n",
        "\n",
        "query = '''\n",
        "CREATE TABLE students_performance (\n",
        "    StudentID INTEGER,\n",
        "    Age INTEGER,\n",
        "    Gender INTEGER,\n",
        "    Ethnicity INTEGER,\n",
        "    ParentalEducation INTEGER,\n",
        "    StudyTimeWeekly DECIMAL(10),\n",
        "    Absences INTEGER,\n",
        "    Tutoring INTEGER,\n",
        "    ParentalSupport INTEGER,\n",
        "    Extracurricular INTEGER,\n",
        "    Sports INTEGER,\n",
        "    Music INTEGER,\n",
        "    Volunteering INTEGER,\n",
        "    GPA DECIMAL(10),\n",
        "    GradeClass INTEGER\n",
        "\n",
        ");\n",
        "\n",
        "'''\n",
        "\n",
        "cursor.execute(query)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "zywQxV77Lnom",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8505a92e-448d-4eff-c5ad-f7ca1bb34b44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sqlite3.Cursor at 0x79fde9ed37c0>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#-------------\n",
        "#Read csv dataset 1\n",
        "#-------------\n",
        "\n",
        "df = pd.read_csv('/content/Student_performance_data _.csv',encoding='ISO-8859-1')\n",
        "df.columns=['StudentID','Age','Gender','Ethnicity','ParentalEducation','StudyTimeWeekly','Absences','Tutoring','ParentalSupport','Extracurricular','Sports','Music','Volunteering','GPA','GradeClass']\n",
        "\n",
        "\n",
        "#-------------\n",
        "#Load the csv into database\n",
        "#-------------\n",
        "\n",
        "df.to_sql('students_performance', conn, if_exists='append', index=False)\n",
        "conn.commit()"
      ],
      "metadata": {
        "id": "B_NYI4JqwMzL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#-------------\n",
        "#Create table 2\n",
        "#-------------\n",
        "\n",
        "query = '''\n",
        "CREATE TABLE popular_songs_2022 (\n",
        "       Track varchar(255),\n",
        "       Album_Name varchar(255),\n",
        "       Artist varchar(255),\n",
        "       ISRC varchar(255),\n",
        "       All_Time_Rank integer,\n",
        "       Track_Score decimal(10),\n",
        "       Spotify_Streams integer ,\n",
        "       Spotify_Playlist_Count integer,\n",
        "       Spotify_Playlist_Reach integer,\n",
        "       Spotify_Popularity integer,\n",
        "       YouTube_Views integer,\n",
        "       YouTube_Likes integer,\n",
        "       TikTok_Posts integer,\n",
        "       TikTok_Likes integer,\n",
        "       TikTok_Views integer,\n",
        "       YouTube_Playlist_Reach integer,\n",
        "       Apple_Music_Playlist_Count integer,\n",
        "       AirPlay_Spins integer,\n",
        "       SiriusXM_Spins integer,\n",
        "       Deezer_Playlist_Count integer,\n",
        "       Deezer_Playlist_Reach integer,\n",
        "       Amazon_Playlist_Count integer,\n",
        "       Pandora_Streams integer,\n",
        "       Pandora_Track_Stations integer,\n",
        "       Soundcloud_Streams integer,\n",
        "       Shazam_Counts integer,\n",
        "       TIDAL_Popularity integer,\n",
        "       Explicit_Track integer\n",
        "\n",
        ");\n",
        "\n",
        "'''\n",
        "\n",
        "cursor.execute(query)\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "_MLV04HtP5bT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ec1208b-7768-4ef8-847f-96d86297e4e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sqlite3.Cursor at 0x79fde9ed37c0>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#-------------\n",
        "#Read csv dataset 2\n",
        "#-------------\n",
        "\n",
        "df2 = pd.read_csv('/content/spotify_songs_2022.csv',encoding = 'latin')\n",
        "df2= df2[['Track', 'Album Name', 'Artist','ISRC',\n",
        "       'All Time Rank', 'Track Score', 'Spotify Streams',\n",
        "       'Spotify Playlist Count', 'Spotify Playlist Reach',\n",
        "       'Spotify Popularity', 'YouTube Views', 'YouTube Likes', 'TikTok Posts',\n",
        "       'TikTok Likes', 'TikTok Views', 'YouTube Playlist Reach',\n",
        "       'Apple Music Playlist Count', 'AirPlay Spins', 'SiriusXM Spins',\n",
        "       'Deezer Playlist Count', 'Deezer Playlist Reach',\n",
        "       'Amazon Playlist Count', 'Pandora Streams', 'Pandora Track Stations',\n",
        "       'Soundcloud Streams', 'Shazam Counts', 'TIDAL Popularity',\n",
        "       'Explicit Track']]\n",
        "df2.columns = ['Track', 'Album_Name', 'Artist', 'ISRC',\n",
        "       'All_Time_Rank', 'Track_Score', 'Spotify_Streams',\n",
        "       'Spotify_Playlist_Count', 'Spotify_Playlist_Reach',\n",
        "       'Spotify_Popularity', 'YouTube_Views', 'YouTube_Likes', 'TikTok_Posts',\n",
        "       'TikTok_Likes', 'TikTok_Views', 'YouTube_Playlist_Reach',\n",
        "       'Apple_Music_Playlist_Count', 'AirPlay_Spins', 'SiriusXM_Spins',\n",
        "       'Deezer_Playlist_Count', 'Deezer_Playlist_Reach',\n",
        "       'Amazon_Playlist_Count', 'Pandora_Streams', 'Pandora_Track_Stations',\n",
        "       'Soundcloud_Streams', 'Shazam_Counts', 'TIDAL_Popularity',\n",
        "       'Explicit_Track']\n",
        "\n",
        "\n",
        "#-------------\n",
        "#Load the csv into database\n",
        "#-------------\n",
        "\n",
        "df2.to_sql('popular_songs_2022', conn, if_exists='append', index=False)\n",
        "conn.commit()\n"
      ],
      "metadata": {
        "id": "Y5kQTvLWuOHg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#-------------\n",
        "# A Quick Check\n",
        "#-------------\n",
        "\n",
        "cursor.execute(\"SELECT * FROM students_performance limit 1\")\n",
        "print(cursor.fetchall())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vCYDH3HDuYNX",
        "outputId": "11949e13-fed7-4809-91a0-42974e6d7109"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(1001, 17, 1, 0, 2, 19.833722807854716, 7, 1, 2, 0, 0, 1, 0, 2.929195591667681, 2)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#-------------\n",
        "# OpenAI Key\n",
        "#-------------\n",
        "\n",
        "os.environ[\"OPENAI_API_KEY\"] = \"openai_key_goes_here\""
      ],
      "metadata": {
        "id": "KIXY17rQLjBf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#-------------\n",
        "# SQL Database Chaining + LLM\n",
        "#-------------\n",
        "\n",
        "db = SQLDatabase.from_uri(\"sqlite:///quantum.db\")\n",
        "llm = OpenAI(temperature=0, verbose=True)\n",
        "db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)"
      ],
      "metadata": {
        "id": "t-1aqfeNLtJN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#-------------\n",
        "# Running the LLM\n",
        "#-------------\n",
        "\n",
        "db_chain.run(\"What's the average performance of the students?\")"
      ],
      "metadata": {
        "id": "1qz3M9qzMP5S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 182
        },
        "outputId": "101da486-03ce-4b85-9251-fc275e905e78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
            "What's the average performance of the students?\n",
            "SQLQuery:\u001b[32;1m\u001b[1;3mSELECT AVG(GPA) FROM students_performance\u001b[0m\n",
            "SQLResult: \u001b[33;1m\u001b[1;3m[(1.9061863027265407,)]\u001b[0m\n",
            "Answer:\u001b[32;1m\u001b[1;3mThe average performance of the students is 1.9061863027265407.\u001b[0m\n",
            "\u001b[1m> Finished chain.\u001b[0m\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'The average performance of the students is 1.9061863027265407.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "db_chain.run(\"What are the main characteristics of highly performing student?\")"
      ],
      "metadata": {
        "id": "yya0Gvh_OCyM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "0e18f168-6b67-4581-938b-014cde30477d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
            "What are the main characteristics of highly performing student?\n",
            "SQLQuery:\u001b[32;1m\u001b[1;3mSELECT \"StudentID\", \"Age\", \"Gender\", \"Ethnicity\", \"ParentalEducation\", \"StudyTimeWeekly\", \"Absences\", \"Tutoring\", \"ParentalSupport\", \"Extracurricular\", \"Sports\", \"Music\", \"Volunteering\", \"GPA\", \"GradeClass\" FROM students_performance ORDER BY \"GPA\" DESC LIMIT 5\u001b[0m\n",
            "SQLResult: \u001b[33;1m\u001b[1;3m[(1045, 18, 1, 0, 1, 18.921512361376628, 1, 1, 3, 1, 1, 0, 0, 4, 0), (1443, 15, 0, 0, 2, 19.424398237709887, 0, 0, 3, 0, 1, 1, 1, 4, 0), (2279, 15, 1, 0, 2, 18.899695711116863, 3, 1, 3, 1, 1, 0, 0, 4, 0), (2706, 18, 1, 0, 2, 8.858281960839271, 0, 1, 4, 1, 0, 0, 0, 4, 0), (2920, 15, 0, 3, 1, 17.442121274931623, 1, 1, 1, 1, 1, 0, 0, 4, 0)]\u001b[0m\n",
            "Answer:\u001b[32;1m\u001b[1;3mThe main characteristics of highly performing students are: being 15-18 years old, being male, having parents with a higher level of education, studying for around 18 hours per week, having low absences, receiving tutoring and parental support, participating in extracurricular activities and sports, and having a high GPA and grade in class.\u001b[0m\n",
            "\u001b[1m> Finished chain.\u001b[0m\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'The main characteristics of highly performing students are: being 15-18 years old, being male, having parents with a higher level of education, studying for around 18 hours per week, having low absences, receiving tutoring and parental support, participating in extracurricular activities and sports, and having a high GPA and grade in class.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "db_chain.run(\"What is the most popular song in 2022?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 182
        },
        "id": "dKc9cNZ5vrJP",
        "outputId": "75c99a56-e560-410a-c151-926bc0d6c339"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
            "What is the most popular song in 2022?\n",
            "SQLQuery:\u001b[32;1m\u001b[1;3mSELECT \"Track\" FROM popular_songs_2022 ORDER BY \"Spotify_Streams\" DESC LIMIT 1;\u001b[0m\n",
            "SQLResult: \u001b[33;1m\u001b[1;3m[('Best Day Of My Life',)]\u001b[0m\n",
            "Answer:\u001b[32;1m\u001b[1;3mBest Day Of My Life\u001b[0m\n",
            "\u001b[1m> Finished chain.\u001b[0m\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Best Day Of My Life'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "db_chain.run(\"How is the weather today?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "1qCOfdY-v2L8",
        "outputId": "91ba6521-0435-496a-f1dc-66c2735fa4a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
            "How is the weather today?\n",
            "SQLQuery:\u001b[32;1m\u001b[1;3mSELECT \"Weather\" FROM weather_forecast WHERE \"Date\" = date('now') LIMIT 1\u001b[0m"
          ]
        },
        {
          "output_type": "error",
          "ename": "OperationalError",
          "evalue": "(sqlite3.OperationalError) no such table: weather_forecast\n[SQL: SELECT \"Weather\" FROM weather_forecast WHERE \"Date\" = date('now') LIMIT 1]\n(Background on this error at: https://sqlalche.me/e/20/e3q8)",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOperationalError\u001b[0m                          Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_exec_single_context\u001b[0;34m(self, dialect, context, statement, parameters)\u001b[0m\n\u001b[1;32m   1966\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mevt_handled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1967\u001b[0;31m                     self.dialect.do_execute(\n\u001b[0m\u001b[1;32m   1968\u001b[0m                         \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr_statement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meffective_parameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sqlalchemy/engine/default.py\u001b[0m in \u001b[0;36mdo_execute\u001b[0;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[1;32m    923\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 924\u001b[0;31m         \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    925\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mOperationalError\u001b[0m: no such table: weather_forecast",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mOperationalError\u001b[0m                          Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-41-db291beb4ac8>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdb_chain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"How is the weather today?\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py\u001b[0m in \u001b[0;36mwarning_emitting_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    166\u001b[0m                 \u001b[0mwarned\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m                 \u001b[0memit_warning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m         \u001b[0;32masync\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mawarning_emitting_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, callbacks, tags, metadata, *args, **kwargs)\u001b[0m\n\u001b[1;32m    598\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    599\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"`run` supports only one positional argument.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 600\u001b[0;31m             return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[\n\u001b[0m\u001b[1;32m    601\u001b[0m                 \u001b[0m_output_key\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    602\u001b[0m             ]\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py\u001b[0m in \u001b[0;36mwarning_emitting_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    166\u001b[0m                 \u001b[0mwarned\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m                 \u001b[0memit_warning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m         \u001b[0;32masync\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mawarning_emitting_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)\u001b[0m\n\u001b[1;32m    381\u001b[0m         }\n\u001b[1;32m    382\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 383\u001b[0;31m         return self.invoke(\n\u001b[0m\u001b[1;32m    384\u001b[0m             \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m             \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRunnableConfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36minvoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    164\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    167\u001b[0m         \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36minvoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    154\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m             outputs = (\n\u001b[0;32m--> 156\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    157\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mnew_arg_supported\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m                 \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_experimental/sql/base.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m    202\u001b[0m             \u001b[0;31m# improvement of few shot prompt seeds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m             \u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintermediate_steps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mintermediate_steps\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 204\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_experimental/sql/base.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m    147\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mSQL_RESULT\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msql_cmd\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m                     \u001b[0msql_cmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msql_cmd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSQL_RESULT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatabase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msql_cmd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    150\u001b[0m                 \u001b[0mintermediate_steps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# output: sql exec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_community/utilities/sql_database.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, command, fetch, include_columns, parameters, execution_options)\u001b[0m\n\u001b[1;32m    500\u001b[0m         \u001b[0mIf\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mstatement\u001b[0m \u001b[0mreturns\u001b[0m \u001b[0mno\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0man\u001b[0m \u001b[0mempty\u001b[0m \u001b[0mstring\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mreturned\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    501\u001b[0m         \"\"\"\n\u001b[0;32m--> 502\u001b[0;31m         result = self._execute(\n\u001b[0m\u001b[1;32m    503\u001b[0m             \u001b[0mcommand\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexecution_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexecution_options\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_community/utilities/sql_database.py\u001b[0m in \u001b[0;36m_execute\u001b[0;34m(self, command, fetch, parameters, execution_options)\u001b[0m\n\u001b[1;32m    465\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    466\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Query expression has unknown type: {type(command)}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 467\u001b[0;31m             cursor = connection.execute(\n\u001b[0m\u001b[1;32m    468\u001b[0m                 \u001b[0mcommand\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    469\u001b[0m                 \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, statement, parameters, execution_options)\u001b[0m\n\u001b[1;32m   1416\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mObjectNotExecutableError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m             return meth(\n\u001b[0m\u001b[1;32m   1419\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m                 \u001b[0mdistilled_parameters\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sqlalchemy/sql/elements.py\u001b[0m in \u001b[0;36m_execute_on_connection\u001b[0;34m(self, connection, distilled_params, execution_options)\u001b[0m\n\u001b[1;32m    513\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mTYPE_CHECKING\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    514\u001b[0m                 \u001b[0;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExecutable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 515\u001b[0;31m             return connection._execute_clauseelement(\n\u001b[0m\u001b[1;32m    516\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistilled_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexecution_options\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    517\u001b[0m             )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_execute_clauseelement\u001b[0;34m(self, elem, distilled_parameters, execution_options)\u001b[0m\n\u001b[1;32m   1638\u001b[0m             \u001b[0mlinting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdialect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompiler_linting\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mcompiler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mWARN_LINTING\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1639\u001b[0m         )\n\u001b[0;32m-> 1640\u001b[0;31m         ret = self._execute_context(\n\u001b[0m\u001b[1;32m   1641\u001b[0m             \u001b[0mdialect\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1642\u001b[0m             \u001b[0mdialect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecution_ctx_cls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_compiled\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001b[0m\n\u001b[1;32m   1844\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exec_insertmany_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdialect\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1845\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1846\u001b[0;31m             return self._exec_single_context(\n\u001b[0m\u001b[1;32m   1847\u001b[0m                 \u001b[0mdialect\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1848\u001b[0m             )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_exec_single_context\u001b[0;34m(self, dialect, context, statement, parameters)\u001b[0m\n\u001b[1;32m   1984\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1985\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1986\u001b[0;31m             self._handle_dbapi_exception(\n\u001b[0m\u001b[1;32m   1987\u001b[0m                 \u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr_statement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meffective_parameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1988\u001b[0m             )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_handle_dbapi_exception\u001b[0;34m(self, e, statement, parameters, cursor, context, is_sub_exec)\u001b[0m\n\u001b[1;32m   2351\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mshould_wrap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2352\u001b[0m                 \u001b[0;32massert\u001b[0m \u001b[0msqlalchemy_exception\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2353\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0msqlalchemy_exception\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2354\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2355\u001b[0m                 \u001b[0;32massert\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_exec_single_context\u001b[0;34m(self, dialect, context, statement, parameters)\u001b[0m\n\u001b[1;32m   1965\u001b[0m                             \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1966\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mevt_handled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1967\u001b[0;31m                     self.dialect.do_execute(\n\u001b[0m\u001b[1;32m   1968\u001b[0m                         \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr_statement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meffective_parameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1969\u001b[0m                     )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sqlalchemy/engine/default.py\u001b[0m in \u001b[0;36mdo_execute\u001b[0;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[1;32m    922\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    923\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 924\u001b[0;31m         \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    925\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    926\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_execute_no_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mOperationalError\u001b[0m: (sqlite3.OperationalError) no such table: weather_forecast\n[SQL: SELECT \"Weather\" FROM weather_forecast WHERE \"Date\" = date('now') LIMIT 1]\n(Background on this error at: https://sqlalche.me/e/20/e3q8)"
          ]
        }
      ]
    }
  ]
}