Merge pull request 'feat/ai-chat

Add core components for Database chat' (#5) from feat/ai-chat into main
Reviewed-on: #5

app/app.py

@@ -1,8 +1,352 @@
-from dash import Dash, html
+import json
+from typing import Any, Dict, Tuple
 
-app = Dash()
+import pandas as pd
+from app_styles import header_style
+from dash import (
+    Dash,
+    Input,
+    Output,
+    State,
+    callback,
+    dash_table,
+    dcc,
+    get_asset_url,
+    html,
+    no_update,
+)
+from dash.exceptions import PreventUpdate
+from data_chat import send_message
+from sql_utils import execute_query, test_db_connection
+
+external_stylesheets = ["https://codepen.io/chriddyp/pen/bWLwgP.css"]
+
+app = Dash(__name__, external_stylesheets=external_stylesheets)
+app.index_string = header_style
+
+notification_md = """
+**Hinweise:**
+
+Aufgrund des sparsamen pricing Tiers kann es einige Sekunden dauern, bis die
+Verbindung zur Datenbank hergestellt wird. Im Falle eines Fehlers gern ein-zwei mal erneut
+versuchen.
+
+GPT-4o kann einige Fehler machen. Sollte dies passieren wird eine Fehlermeldung angezeigt.
+In diesem Fall lohnt es sich oft, die Anfrage leicht verändert erneut zu stellen und evtl
+zusätzliche Informationen zu geben.
+
+Das Modell ist dazu aufgefordert, den Output stets auf 100 Zeilen zu begrenzen.
+
+Alle Daten sind komplett zufällig generiert und haben keine Beziehung zu realen Personen.
+
+**Beispielfragen**:
+- Wie viele Kunden haben wir in Hannover?
+- Zeige alle Kunden in Bremen.
+- Berechne den gesamten Stromverbrauch aller Kunden in Magdeburg.
+- Zeige alle Kunden, die zwischen 2021 und 2022 mindestens 200 Kubikmeter Gas verbraucht haben.
+- Wie viele Kunden haben zwischen 2021 und 2022 weniger Strom verbraucht als zwischen 2022
+und 2023?
+
+Weitere Informationen zu den Daten, dem Code sowie zur Nutzung befinden sich in der README im
+[GiTea Repository](https://gitea.captain.particlephysics.de/quadfaselt/grid_application).
+"""
+
+err_style = {
+    "height": "0px",
+    "overflow": "hidden",
+    "transition": "height 0.5s ease-in-out",
+    "border-radius": "15px",
+    "background-color": "#FFCCCB",
+    "text-align": "center",
+    "color": "#FF6B6B",
+    "margin-top": "20px",
+    "margin-left": "20px",
+    "margin-right": "20px",
+    "font-weight": "bold",
+    "display": "flex",
+    "justify-content": "center",
+    "align-items": "center",
+}
+
+
+def render_table(df: pd.DataFrame) -> dash_table.DataTable:
+    """Create a Dash DataTable from a pandas DataFrame.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        The input DataFrame to be rendered as a table.
+
+    Returns
+    -------
+    dash_table.DataTable
+        A Dash DataTable component with styled layout and pagination.
+    """
+    tab = dash_table.DataTable(
+        id="table",
+        columns=[{"name": i, "id": i} for i in df.columns],
+        data=df.to_dict("records"),
+        page_size=10,
+        style_table={
+            "overflowX": "auto",
+            "margin": "auto",
+            "width": "96%",
+            "margin-top": "20px",
+        },
+        style_cell={
+            "minWidth": "100px",
+            "width": "150px",
+            "maxWidth": "300px",
+            "overflow": "hidden",
+            "textOverflow": "ellipsis",
+        },
+        style_header={
+            "backgroundColor": "lightblue",
+            "fontWeight": "bold",
+            "color": "black",
+        },
+    )
+    return tab
+
+
+def get_layout() -> html.Div:
+    """Generate the layout for a Dash application.
+
+    This function creates a complex layout for a database chat interface
+    and a direct SQL query interface. It includes various Dash components
+    such as text areas, buttons, and a loading spinner.
+
+    The layout consists of:
+    - A header with title and logo
+    - A textarea for user input (database chat)
+    - A submit button for the database chat
+    - A Notification about the connection time and LLM performance
+    - An error message area
+    - A loading spinner and output area for database responses
+    - A section for direct SQL queries, including a textarea and submit button
+    - An output area for SQL query results
+
+    Returns
+    -------
+    html.Div
+        A Dash html.Div component containing the entire layout of the application.
+    """
+    global err_style
+    tmp_style = err_style.copy()
+    tmp_style["height"] = "0px"
+
+    start_value = "Stelle deine Frage an die Datenbank..."
+
+    layout = html.Div(
+        [
+            html.Div(
+                [
+                    html.H1("Datenbank-Chat", className="heading"),
+                    html.Img(src=get_asset_url("logo.png"), className="logo"),
+                ],
+                className="header-container",
+            ),  # Header
+            html.Div(
+                "Ganz ohne SQL-Kenntnisse Daten zu Zählerstandmessungen unserer Kunden abrufen!",
+                style={"margin-left": "20px", "font-weight": "bold", "font-size": "20px"},
+            ),
+            dcc.Store(
+                id="tmp-value", data=start_value, storage_type="memory"
+            ),  # Store previous prompt
+            dcc.Textarea(
+                id="input-field",
+                value=start_value,
+                style={"width": "96%", "height": 200, "margin-left": "20px"},
+            ),  # Input field
+            html.Div([]),  # Needed for keeping the layout clean
+            html.Button(
+                "Abschicken",
+                id="submit-button",
+                n_clicks=0,
+                disabled=False,
+                style={"margin-left": "20px"},
+            ),  # Submit button
+            dcc.Markdown(
+                notification_md,
+                style={
+                    "margin-left": "20px",
+                    "margin-top": "20px",
+                    "margin-right": "10px",
+                    "background-color": "#C7E6F5",
+                    "border-radius": "5px",
+                    "padding": "10px",
+                },
+            ),
+            html.Div(
+                [html.P("Bitte eine neue Anfrage eingeben.")], id="error", style=tmp_style
+            ),  # Error message (only visible if input is not updated but submit button is clicked)
+            dcc.Loading(
+                id="loading",
+                type="default",
+                children=[
+                    html.Div(
+                        "Hier erscheint die Antwort des KI-Modells.",
+                        id="text-output",
+                        style={
+                            "whiteSpace": "pre-line",
+                            "margin-top": "30px",
+                            "margin-left": "20px",
+                            "margin-right": "20px",
+                            "border": "2px solid #86bc25",
+                            "border-radius": "15px",
+                            "padding": "20px",
+                        },
+                    )
+                ],
+            ),
+            html.H2(
+                "Direkte SQL-Abfrage",
+                style={"margin-left": "20px", "margin-top": "50px", "font-size": 24},
+            ),  # SQL Header
+            html.Div(
+                (
+                    "Hier kann der ausgegebene SQL-Code getestet oder mit selbst"
+                    "geschriebenen Code verglichen werden."
+                ),
+                style={"margin-left": "20px", "font-weight": "bold", "font-size": "16px"},
+            ),
+            dcc.Textarea(
+                id="sql-input-field",
+                value="(Microsoft) SQL-Abfrage eingeben...",
+                style={"width": "96%", "height": 200, "margin-left": "20px"},
+            ),  # SQL Input field
+            html.Div([]),  # Needed for keeping the layout clean
+            html.Button(
+                "Abschicken",
+                id="sql-submit-button",
+                n_clicks=0,
+                disabled=False,
+                style={"margin-left": "20px"},
+            ),  # Submit button
+            html.Div(id="sql-output", style={"margin-top": "10px"}),  # SQL Output
+        ],
+        className="container",
+    )
+
+    return layout
+
+
+app.layout = html.Div([get_layout()])
+
+
+@callback(
+    Output("text-output", "children"),
+    Output("tmp-value", "data"),
+    Output("error", "style"),
+    Output("error", "children"),
+    Input("submit-button", "n_clicks"),
+    State("input-field", "value"),
+    State("tmp-value", "data"),
+    prevent_initial_call=True,
+    running=[
+        (Output("submit-button", "disabled"), True, False),
+        (
+            Output("submit-button", "style"),
+            {"opacity": 0.5, "margin-left": "20px"},
+            {"opacity": 1.0, "margin-left": "20px"},
+        ),
+    ],
+)
+def update_output(n_clicks: int, value: str, data: str) -> Tuple[Any, Any, Dict[str, str], Any]:
+    """Update the output based on user input and button clicks.
+
+    Parameters
+    ----------
+    n_clicks : int
+        Number of times the submit button has been clicked.
+    value : str
+        Current value of the input field.
+    data : str
+        Previously stored value.
+
+    Returns
+    -------
+    Tuple[str, str, Dict[str, Any]]
+        Updated output text, new stored value, and error style.
+    """
+    global err_style
+    db_connected = test_db_connection()
+    if n_clicks > 0 and value != data and db_connected:
+        result = send_message(value)
+        err_style["height"] = "0px"
+
+        # parse LLM response to dict, then try to execute the query
+        try:
+            parsed_result = json.loads(result, strict=False)
+
+            result_table = execute_query(parsed_result["query"])
+            children = [
+                html.P([html.B("Zusammenfassung: "), f"{parsed_result['summary']}"]),
+                html.P([html.B("SQL Abfrage: "), f"{parsed_result['query']}"]),
+                render_table(result_table),
+            ]
+            return children, value, err_style, html.P("")
+        except Exception as e:
+            err_style["height"] = "400px"
+            err_child = html.Div(f"Folgender Fehler ist aufgetreten: {e}.LLM Output: {result}.")
+
+            return no_update, value, err_style, err_child
+
+    elif not db_connected:
+        err_style["height"] = "50px"
+        err_child = html.P(
+            (
+                "Fehler beim Herstellen der Verbindung zur "
+                "Datenbank. Bitte versuche es später erneut."
+            )
+        )
+        return no_update, no_update, err_style, err_child
+
+    elif value == data:
+
+        err_style["height"] = "50px"
+        err_child = html.P("Bitte eine neue Anfrage eingeben.")
+        return no_update, no_update, err_style, err_child
+
+    raise PreventUpdate
+
+
+@callback(
+    Output("sql-output", "children"),
+    Input("sql-submit-button", "n_clicks"),
+    State("sql-input-field", "value"),
+    prevent_initial_call=True,
+)
+def run_sql_query(n_clicks: int, value: str) -> str:
+    """Run a SQL query and return the results.
+
+    Parameters
+    ----------
+    n_clicks : int
+        Number of times the submit button has been clicked.
+    value : str
+        Current value of the input field.
+
+    Returns
+    -------
+    str
+        The results of the SQL query.
+    """
+    if n_clicks > 0:
+        result = execute_query(value)
+        if isinstance(result, str):
+            global err_style
+            tmp_style = err_style.copy()
+            tmp_style["height"] = "100px"
+            tmp_style["padding"] = "20px"
+            err_child = html.Div(
+                [html.P(f"Fehler bei der Ausführung der Abfrage: {result}")], style=tmp_style
+            )
+            return err_child
+        else:
+            return render_table(result)
+    raise PreventUpdate
 
-app.layout = [html.Div(children="Hello World")]
 
 server = app.server
 

app/app_styles.py

@@ -0,0 +1,37 @@
+header_style = """
+<!DOCTYPE html>
+<html>
+    <head>
+        {%metas%}
+        <title>{%title%}</title>
+        {%favicon%}
+        {%css%}
+        <style>
+            .header-container {
+                display: flex;
+                justify-content: space-between;
+                align-items: center;
+                padding: 20px;
+                background-color: #ffffff;
+            }
+            .heading {
+                font-size: 2.5em;
+                font-weight: bold;
+                color: #333;
+            }
+            .logo {
+                height: 30px;
+                width: auto;
+            }
+        </style>
+    </head>
+    <body>
+        {%app_entry%}
+        <footer>
+            {%config%}
+            {%scripts%}
+            {%renderer%}
+        </footer>
+    </body>
+</html>
+"""

app/data_chat.py

@@ -0,0 +1,117 @@
+import os
+
+from openai import OpenAI
+
+# from openai import AzureOpenAI
+
+# Set up credentials
+# NOTE: Usually I would use AzureOpenAI, but due to heavy rate
+# limitations on azure trial accounts, I am using OpenAI directly
+# for this project. However, this is how it would look like for
+# AzureOpenAI (credentials must be provided to environment):
+# client = AzureOpenAI(
+#     azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
+#     api_key=os.getenv("AZURE_OPENAI_KEY"),
+#     api_version="2024-02-01",
+# )
+#  MODEL = "sqlai"  # deployment name
+
+# Set up the OpenAI client
+MODEL = "gpt-4o"
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+
+def send_message(message: str) -> str:
+    """Send a message to the openai chat completion API and return the response.
+
+    Parameters
+    ----------
+    message : str
+        The user's message to be sent to the chat completion API.
+
+    Returns
+    -------
+    str
+        The content of the assistant's response message.
+    """
+    system_message = """
+    Du bist ein hilfsbereiter, fröhlicher Datenbankassistent.
+    Du hilfst Benutzern bei der Erstellung von SQL-Abfragen für eine Datenbank eines
+    großen Energieversorgungsunternehmens. Die Datenbank enthält Tabellen für Adressen,
+    Zähler, Kunden und Ablesungen. Es werden Gaszähler (MeterType 'GAS') und Stromzähler
+    (MeterType 'ELT')unterschieden.
+
+    Besonders wichtig ist, dass die Ablesungen der Werte kumulativ sind. Wenn nach dem Verbrauch
+    gefragt wird, sollte der Unterschied zwischen zwei aufeinanderfolgenden Ablesungen berechnet
+    werden.
+
+    Verwende beim Erstellen Ihrer Antworten das folgende Datenbankschema:
+
+    CREATE TABLE Addresses (
+        ID INT PRIMARY KEY IDENTITY(1,1),
+        StreetName NVARCHAR(100),
+        HouseNumber NVARCHAR(10),
+        City NVARCHAR(50),
+        PostalCode NVARCHAR(10),
+        Longitude FLOAT,
+        Latitude FLOAT
+    );
+
+    CREATE TABLE Meters (
+        ID INT PRIMARY KEY IDENTITY(1,1),
+        Signature NVARCHAR(11),
+        MeterType NVARCHAR(3),
+        AddressID INT,
+        FOREIGN KEY (AddressID) REFERENCES Addresses(ID)
+    );
+
+    CREATE TABLE Customers (
+        ID INT PRIMARY KEY IDENTITY(1,1),
+        FirstName NVARCHAR(100),
+        LastName NVARCHAR(100),
+        GasMeterID INT,
+        EltMeterID INT,
+        FOREIGN KEY (GasMeterID) REFERENCES Meters(ID),
+        FOREIGN KEY (EltMeterID) REFERENCES Meters(ID)
+    );
+
+    CREATE TABLE Readings (
+        ID INT PRIMARY KEY IDENTITY(1,1),
+        CustomerID INT,
+        MeterID INT,
+        ReadingDate DATE,
+        ReadingValue INT,
+        FOREIGN KEY (CustomerID) REFERENCES Customers(ID),
+        FOREIGN KEY (MeterID) REFERENCES Meters(ID)
+    );
+
+    Füge Spaltenüberschriften in die Abfrageergebnisse ein.
+
+    Gib deine Antwort immer im folgenden JSON-Format an:
+
+    { "summary": "your-summary", "query":  "your-query" }
+
+    Gib NUR JSON aus.
+    Ersetze in der vorangehenden JSON-Antwort "your-query" durch die Microsoft SQL Server Query,
+    um die angeforderten Daten abzurufen.
+    Ersetze in der vorangehenden JSON-Antwort "your-summary" durch eine Zusammenfassung der Abfrage.
+    Gib immer alle Spalten der Tabelle an.
+    Wenn die resultierende Abfrage nicht ausführbar ist, ersetze "your-query“ durch NA, aber ersetze
+    trotzdem "your-query" durch eine Zusammenfassung der Abfrage.
+    Verwende KEINE MySQL-Syntax, sondern AUSSCHLIESSLICH Microsoft SQL.
+    Begrenze die SQL-Abfrage immer auf 100 Zeilen.
+    Formatiere den Output bestmöglich.
+    """
+
+    response = client.chat.completions.create(
+        model=MODEL,
+        messages=[
+            {"role": "system", "content": system_message},
+            {"role": "user", "content": message},
+        ],
+    )
+
+    result_str = response.choices[0].message.content.replace("```json\n", "").replace("```", "")
+    if ("\n") not in result_str:
+        result_str = result_str.replace("\\", "\n")
+    return result_str

app/sql_utils.py

@@ -0,0 +1,66 @@
+import os
+from typing import Union
+
+import pandas as pd
+import pyodbc
+
+
+def test_db_connection() -> bool:
+    """Test the connection to Azure SQL Database.
+
+    This function attempts to establish a connection to an Azure SQL Database
+    using the connection string stored in the environment variable
+    'AZURE_SQL_CONNECTION_STRING'. It makes up to 5 attempts to connect,
+    with a timeout of 480 seconds for each attempt.
+
+    Returns
+    -------
+    bool
+        True if the connection was successful, False otherwise.
+    """
+    connection_string = os.environ.get("AZURE_SQL_CONNECTION_STRING")
+    for i in range(5):
+        print(f"Trying to connect to Azure SQL Database... Attempt {i + 1}")
+        try:
+            pyodbc.connect(connection_string, timeout=480)
+            print("Connected to Azure SQL Database successfully!")
+            connected = True
+            break
+
+        except Exception as e:
+            print(f"Error connecting to Azure SQL Database: {e}")
+            connected = False
+            continue
+
+    return connected
+
+
+def execute_query(query: str) -> Union[pd.DataFrame, str]:
+    """Execute a SQL query on an Azure SQL Database and return the results.
+
+    This function connects to an Azure SQL Database using the connection string
+    stored in the environment variable 'AZURE_SQL_CONNECTION_STRING', executes
+    the provided SQL query, and returns the results as a pandas DataFrame.
+
+    Parameters
+    ----------
+    query : str
+        The SQL query to execute.
+
+    Returns
+    -------
+    Union[pd.DataFrame, str]
+        A pandas DataFrame containing the query results if successful,
+        or a string containing the error message if an exception occurs.
+    """
+    try:
+        connection_string = os.environ.get("AZURE_SQL_CONNECTION_STRING")
+        conn = pyodbc.connect(connection_string, timeout=240)
+        df = pd.read_sql(query, conn)
+        conn.close()
+        return df
+    except Exception as e:
+        return str(e)
+    finally:
+        if conn in locals():
+            conn.close()

poetry.lock

@@ -11,6 +11,39 @@ files = [
     {file = "alabaster-1.0.0.tar.gz", hash = "sha256:c00dca57bca26fa62a6d7d0a9fcce65f3e026e9bfe33e9c538fd3fbb2144fd9e"},
 ]
 
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
+    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
+]
+
+[[package]]
+name = "anyio"
+version = "4.4.0"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
+    {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
+]
+
+[package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
+idna = ">=2.8"
+sniffio = ">=1.1"
+typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
+
+[package.extras]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.23)"]
+
 [[package]]
 name = "babel"
 version = "2.16.0"
@@ -305,6 +338,17 @@ files = [
     {file = "distlib-0.3.8.tar.gz", hash = "sha256:1530ea13e350031b6312d8580ddb6b27a104275a31106523b8f123787f494f64"},
 ]
 
+[[package]]
+name = "distro"
+version = "1.9.0"
+description = "Distro - an OS platform information API"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2"},
+    {file = "distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed"},
+]
+
 [[package]]
 name = "docutils"
 version = "0.21.2"
@@ -422,6 +466,63 @@ setproctitle = ["setproctitle"]
 testing = ["coverage", "eventlet", "gevent", "pytest", "pytest-cov"]
 tornado = ["tornado (>=0.2)"]
 
+[[package]]
+name = "h11"
+version = "0.14.0"
+description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
+    {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
+]
+
+[[package]]
+name = "httpcore"
+version = "1.0.5"
+description = "A minimal low-level HTTP client."
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "pydantic_core-2.20.1.tar.gz", hash = "sha256:26ca695eeee5f9f1aeeb211ffc12f10bcb6f71e2989988fda61dabd65db878d4"},
+]
+
+[package.dependencies]
+typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
+
 [[package]]
 name = "pyflakes"
 version = "3.2.0"
@@ -1460,6 +1778,17 @@ files = [
     {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
 ]
 
+[[package]]
+name = "sniffio"
+version = "1.3.1"
+description = "Sniff out which async library your code is running under"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"},
+    {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
+]
+
 [[package]]
 name = "snowballstemmer"
 version = "2.2.0"
@@ -1640,6 +1969,26 @@ files = [
     {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
 ]
 
+[[package]]
+name = "tqdm"
+version = "4.66.5"
+description = "Fast, Extensible Progress Meter"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "tqdm-4.66.5-py3-none-any.whl", hash = "sha256:90279a3770753eafc9194a0364852159802111925aa30eb3f9d85b0e805ac7cd"},
+    {file = "tqdm-4.66.5.tar.gz", hash = "sha256:e1020aef2e5096702d8a025ac7d16b1577279c9d63f8375b63083e9a5f0fcbad"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
+notebook = ["ipywidgets (>=6)"]
+slack = ["slack-sdk"]
+telegram = ["requests"]
+
 [[package]]
 name = "types-requests"
 version = "2.32.0.20240712"
@@ -1794,4 +2143,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "3f76ad143c2f6e35163d1bc8d123b97b9bf2a96f86914d240e0c7da2226f1d85"
+content-hash = "a6af9cf3858b3e05a3ac81716c79d6a3ed8e797da13391b2fc3e8c383741d00b"

pyproject.toml

@@ -34,6 +34,7 @@ dash = "^2.17.1"
 gunicorn = "^23.0.0"
 pyodbc = "^5.1.0"
 pandas = "^2.2.2"
+openai = "^1.43.0"
 
 [tool.poetry.group.docs.dependencies]
 mkdocs = "^1.6.0"