feat/ai-chat: Add core components for Database chat #5
143
app/app.py
143
app/app.py
@@ -6,6 +6,7 @@ from dash import (
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Output,
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State,
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callback,
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dash_table,
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dcc,
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get_asset_url,
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html,
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@@ -15,12 +16,14 @@ from dash.exceptions import PreventUpdate
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from .app_styles import header_style
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from .data_chat import send_message
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from .sql_utils import execute_query, test_db_connection
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external_stylesheets = ["https://codepen.io/chriddyp/pen/bWLwgP.css"]
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app = Dash(__name__, external_stylesheets=external_stylesheets)
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app.index_string = header_style
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err_style = {
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"height": "0px",
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"overflow": "hidden",
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@@ -38,8 +41,35 @@ err_style = {
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"align-items": "center",
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}
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def get_layout() -> html.Div:
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"""Generate the layout for a Dash application.
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This function creates a complex layout for a database chat interface
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and a direct SQL query interface. It includes various Dash components
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such as text areas, buttons, and a loading spinner.
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The layout consists of:
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- A header with title and logo
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- A textarea for user input (database chat)
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- A submit button for the database chat
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- An error message area
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- A loading spinner and output area for database responses
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- A section for direct SQL queries, including a textarea and submit button
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- An output area for SQL query results
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Returns
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-------
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html.Div
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A Dash html.Div component containing the entire layout of the application.
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"""
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global err_style
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tmp_style = err_style.copy()
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tmp_style["height"] = "0px"
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start_value = "Stelle deine Frage an die Datenbank..."
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app.layout = html.Div(
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layout = html.Div(
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[
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html.Div(
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[
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@@ -49,7 +79,7 @@ app.layout = html.Div(
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className="header-container",
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), # Header
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dcc.Store(
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id="tmp-value", data=start_value, storage_type="session"
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id="tmp-value", data=start_value, storage_type="memory"
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), # Store previous prompt
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dcc.Textarea(
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id="input-field",
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@@ -65,7 +95,7 @@ app.layout = html.Div(
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style={"margin-left": "20px"},
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), # Submit button
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html.Div(
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[html.P("Bitte eine neue Anfrage eingeben.")], id="error", style=err_style
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[html.P("Bitte eine neue Anfrage eingeben.")], id="error", style=tmp_style
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), # Error message (only visible if input is not updated but submit button is clicked)
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dcc.Loading(
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id="loading",
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@@ -86,15 +116,39 @@ app.layout = html.Div(
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)
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],
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),
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], # Output field
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html.H2(
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"Direkte SQL-Abfrage",
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style={"margin-left": "20px", "margin-top": "50px", "font-size": 24},
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), # SQL Header
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dcc.Textarea(
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id="sql-input-field",
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value="(Microsoft) SQL-Abfrage eingeben...",
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style={"width": "96%", "height": 200, "margin-left": "20px"},
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), # SQL Input field
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html.Div([]), # Needed for keeping the layout clean
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html.Button(
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"Abschicken",
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id="sql-submit-button",
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n_clicks=0,
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disabled=False,
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style={"margin-left": "20px"},
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), # Submit button
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html.Div(id="sql-output", style={"margin-top": "10px"}), # SQL Output
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],
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className="container",
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)
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return layout
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app.layout = html.Div([get_layout()])
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@callback(
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Output("text-output", "children"),
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Output("tmp-value", "data"),
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Output("error", "style"),
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Output("error", "children"),
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Input("submit-button", "n_clicks"),
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State("input-field", "value"),
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State("tmp-value", "data"),
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@@ -108,7 +162,7 @@ app.layout = html.Div(
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),
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],
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)
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def update_output(n_clicks: int, value: str, data: str) -> Tuple[str, str, Dict[str, Any]]:
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def update_output(n_clicks: int, value: str, data: str) -> Tuple[Any, Any, Dict[str, str], Any]:
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"""Update the output based on user input and button clicks.
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Parameters
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@@ -126,18 +180,91 @@ def update_output(n_clicks: int, value: str, data: str) -> Tuple[str, str, Dict[
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Updated output text, new stored value, and error style.
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"""
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global err_style
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if n_clicks > 0 and value != data:
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print(f"Value: {value}")
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print(f"Data: {data}")
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db_connected = test_db_connection()
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if n_clicks > 0 and value != data and db_connected:
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result = send_message(value)
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err_style["height"] = "0px"
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return result, value, err_style
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return result, value, err_style, html.P("")
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elif value == data:
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err_style["height"] = "50px"
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return no_update, no_update, err_style
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err_child = html.P("Bitte eine neue Anfrage eingeben.")
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return no_update, no_update, err_style, err_child
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elif not db_connected:
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err_style["height"] = "50px"
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err_child = html.P(
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(
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"Fehler beim Herstellen der Verbindung zur "
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"Datenbank. Bitte versuche es später erneut."
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)
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)
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return no_update, no_update, err_style, err_child
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raise PreventUpdate
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@callback(
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Output("sql-output", "children"),
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Input("sql-submit-button", "n_clicks"),
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State("sql-input-field", "value"),
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prevent_initial_call=True,
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)
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def run_sql_query(n_clicks: int, value: str) -> str:
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"""Run a SQL query and return the results.
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Parameters
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----------
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n_clicks : int
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Number of times the submit button has been clicked.
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value : str
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Current value of the input field.
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Returns
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-------
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str
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The results of the SQL query.
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"""
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if n_clicks > 0:
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result = execute_query(value)
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if isinstance(result, str):
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global err_style
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tmp_style = err_style.copy()
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tmp_style["height"] = "80px"
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tmp_style["padding"] = "20px"
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err_child = html.Div(
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[html.P(f"Fehler bei der Ausführung der Abfrage: {result}")], style=tmp_style
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)
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return err_child
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else:
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table_child = dash_table.DataTable(
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id="table",
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columns=[{"name": i, "id": i} for i in result.columns],
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data=result.to_dict("records"),
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page_size=10,
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style_table={
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"overflowX": "auto",
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"margin": "auto",
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"width": "96%",
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},
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style_cell={
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"minWidth": "100px",
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"width": "150px",
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"maxWidth": "300px",
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"overflow": "hidden",
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"textOverflow": "ellipsis",
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},
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style_header={
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"backgroundColor": "lightblue",
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"fontWeight": "bold",
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"color": "black",
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},
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)
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return table_child
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raise PreventUpdate
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server = app.server
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if __name__ == "__main__":
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@@ -12,7 +12,7 @@ header_style = """
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justify-content: space-between;
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align-items: center;
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padding: 20px;
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background-color: #f8f9fa;
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background-color: #ffffff;
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}
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.heading {
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font-size: 2.5em;
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@@ -26,10 +26,33 @@ def send_message(message: str) -> str:
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str
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The content of the assistant's response message.
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"""
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system_message = """
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Du bist ein hilfsbereiter, fröhlicher Datenbankassistent.
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Verwende beim Erstellen Ihrer Antworten das folgende Datenbankschema:
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MEIN_DATENBANKSCHEMA
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Füge Spaltenüberschriften in die Abfrageergebnisse ein.
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Gib deine Antwort immer im folgenden JSON-Format an:
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JSON FORMAT
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Gib NUR JSON aus.
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Ersetze in der vorangehenden JSON-Antwort "your-query" durch die Microsoft SQL Server Query,
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um die angeforderten Daten abzurufen.
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Ersetze in der vorangehenden JSON-Antwort "your-summary" durch eine Zusammenfassung der Abfrage.
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Gib immer alle Spalten der Tabelle an.
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Wenn die resultierende Abfrage nicht ausführbar ist, ersetze "your-query“ durch NA, aber ersetze
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trotzdem "your-query" durch eine Zusammenfassung der Abfrage.
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Verwende KEINE MySQL-Syntax.
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Begrenze die SQL-Abfrage immer auf 100 Zeilen.
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"""
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system_message = "Du bist ein hilfreicher Assistent."
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response = client.chat.completions.create(
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model=deployment_name,
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messages=[
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{"role": "system", "content": "Du bist ein hilfreicher Assistent."},
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{"role": "system", "content": system_message},
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{"role": "user", "content": message},
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],
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)
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65
app/sql_utils.py
Normal file
65
app/sql_utils.py
Normal file
@@ -0,0 +1,65 @@
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import os
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from typing import Union
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import pandas as pd
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import pyodbc
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def test_db_connection() -> bool:
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"""Test the connection to Azure SQL Database.
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This function attempts to establish a connection to an Azure SQL Database
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using the connection string stored in the environment variable
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'AZURE_SQL_CONNECTION_STRING'. It makes up to 5 attempts to connect,
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with a timeout of 240 seconds for each attempt.
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Returns
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-------
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bool
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True if the connection was successful, False otherwise.
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"""
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connection_string = os.environ.get("AZURE_SQL_CONNECTION_STRING")
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for i in range(5):
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print(f"Trying to connect to Azure SQL Database... Attempt {i + 1}")
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try:
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pyodbc.connect(connection_string, timeout=240)
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print("Connected to Azure SQL Database successfully!")
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connected = True
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break
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except pyodbc.Error as e:
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print(f"Error connecting to Azure SQL Database: {e}")
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connected = False
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return connected
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def execute_query(query: str) -> Union[pd.DataFrame, str]:
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"""Execute a SQL query on an Azure SQL Database and return the results.
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This function connects to an Azure SQL Database using the connection string
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stored in the environment variable 'AZURE_SQL_CONNECTION_STRING', executes
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the provided SQL query, and returns the results as a pandas DataFrame.
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Parameters
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----------
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query : str
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The SQL query to execute.
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Returns
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-------
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Union[pd.DataFrame, str]
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A pandas DataFrame containing the query results if successful,
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or a string containing the error message if an exception occurs.
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"""
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try:
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connection_string = os.environ.get("AZURE_SQL_CONNECTION_STRING")
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conn = pyodbc.connect(connection_string, timeout=240)
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df = pd.read_sql(query, conn)
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conn.close()
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return df
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except Exception as e:
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return str(e)
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finally:
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if conn in locals():
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conn.close()
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