climateqa/engine/talk_to_data/workflow/ipcc.py

import os

from typing import Any
import asyncio
from climateqa.engine.llm import get_llm
from climateqa.engine.talk_to_data.input_processing import find_param, find_relevant_plots, find_relevant_tables_per_plot
from climateqa.engine.talk_to_data.query import execute_sql_query, find_indicator_column
from climateqa.engine.talk_to_data.objects.plot import Plot
from climateqa.engine.talk_to_data.objects.states import State, TTDOutput
from climateqa.engine.talk_to_data.ipcc.config import IPCC_TABLES, IPCC_INDICATOR_COLUMNS_PER_TABLE, IPCC_PLOT_PARAMETERS
from climateqa.engine.talk_to_data.ipcc.plots import IPCC_PLOTS

ROOT_PATH = os.path.dirname(os.path.dirname(os.getcwd()))

async def process_output(
    output_title: str,
    table: str,
    plot: Plot,
    params: dict[str, Any]
) -> tuple[str, TTDOutput, dict[str, bool]]:
    """
    Process a table for a given plot and parameters: builds the SQL query, executes it,
    and generates the corresponding figure.

    Args:
        output_title (str): Title for the output (used as key in outputs dict).
        table (str): The name of the table to process.
        plot (Plot): The plot object containing SQL query and visualization function.
        params (dict[str, Any]): Parameters used for querying the table.

    Returns:
        tuple: (output_title, results dict, errors dict)
    """
    results: TTDOutput = {
        'status': 'OK',
        'plot': plot,
        'table': table,
        'sql_query': None,
        'dataframe': None,
        'figure': None, 
        'plot_information': None,
    }
    errors = {
        'have_sql_query': False,
        'have_dataframe': False
    }

    # Find the indicator column for this table
    indicator_column = find_indicator_column(table, IPCC_INDICATOR_COLUMNS_PER_TABLE)
    if indicator_column:
        params['indicator_column'] = indicator_column

    # Build the SQL query
    sql_query = plot['sql_query'](table, params)
    if not sql_query:
        results['status'] = 'ERROR'
        return output_title, results, errors

    results['plot_information'] = plot['plot_information'](table, params)

    results['sql_query'] = sql_query
    errors['have_sql_query'] = True

    # Execute the SQL query
    df = await execute_sql_query(sql_query)
    if df is not None and not df.empty:
        results['dataframe'] = df
        errors['have_dataframe'] = True
    else:
        results['status'] = 'NO_DATA'

    # Generate the figure (always, even if df is empty, for consistency)
    results['figure'] = plot['plot_function'](params)

    return output_title, results, errors

async def ipcc_workflow(user_input: str) -> State:
    """
    Performs the complete workflow of Talk To IPCC: from user input to SQL queries, dataframes, and figures.

    Args:
        user_input (str): The user's question.

    Returns:
        State: Final state with all the results and error messages if any.
    """
    state: State = {
        'user_input': user_input,
        'plots': [],
        'outputs': {},
        'error': ''
    }

    llm = get_llm(provider="openai")
    plots = await find_relevant_plots(state, llm, IPCC_PLOTS)
    state['plots'] = plots

    if not plots:
        state['error'] = 'There is no plot to answer to the question'
        return state

    errors = {
        'have_relevant_table': False,
        'have_sql_query': False,
        'have_dataframe': False
    }
    outputs = {}

    # Find relevant tables for each plot and prepare outputs
    for plot_name in plots:
        plot = next((p for p in IPCC_PLOTS if p['name'] == plot_name), None)
        if plot is None:
            continue

        relevant_tables = await find_relevant_tables_per_plot(state, plot, llm, IPCC_TABLES)
        if relevant_tables:
            errors['have_relevant_table'] = True

        for table in relevant_tables:
            output_title = f"{plot['short_name']} - {' '.join(table.capitalize().split('_'))}"
            outputs[output_title] = {
                'table': table,
                'plot': plot,
                'status': 'OK'
            }

    # Gather all required parameters
    params = {}
    for param_name in IPCC_PLOT_PARAMETERS:
        param = await find_param(state, param_name, mode='IPCC')
        if param:
            params.update(param)

    # Process all outputs in parallel using process_output
    tasks = [
        process_output(output_title, output['table'], output['plot'], params.copy())
        for output_title, output in outputs.items()
    ]
    results = await asyncio.gather(*tasks)

    # Update outputs with results and error flags
    for output_title, task_results, task_errors in results:
        outputs[output_title]['sql_query'] = task_results['sql_query']
        outputs[output_title]['dataframe'] = task_results['dataframe']
        outputs[output_title]['figure'] = task_results['figure']
        outputs[output_title]['plot_information'] = task_results['plot_information']
        outputs[output_title]['status'] = task_results['status']
        errors['have_sql_query'] |= task_errors['have_sql_query']
        errors['have_dataframe'] |= task_errors['have_dataframe']

    state['outputs'] = outputs

    # Set error messages if needed
    if not errors['have_relevant_table']:
        state['error'] = "There is no relevant table in our database to answer your question"
    elif not errors['have_sql_query']:
        state['error'] = "There is no relevant sql query on our database that can help to answer your question"
    elif not errors['have_dataframe']:
        state['error'] = "There is no data in our table that can answer to your question"

    return state