"""
Text-to-SQL Agent Evaluation Framework

This module provides a comprehensive framework for evaluating Text-to-SQL agents using Ragas.
It includes dataset preparation, agent implementation, evaluation metrics, and error analysis tools.

Key Components:
- Text2SQLAgent: Core agent implementation with OpenAI integration
- Dataset utilities for BookSQL and custom datasets
- Database interface for SQLite query execution
- Ragas-based evaluation framework with custom metrics
- Error analysis and validation tools

Usage:
    import asyncio
    from openai import AsyncOpenAI
    from ragas_examples.text2sql import Text2SQLAgent, execute_sql, text2sql_experiment, load_dataset
    
    # Create and use agent
    client = AsyncOpenAI(api_key="your-api-key")
    agent = Text2SQLAgent(client=client, model_name="gpt-5-mini")
    result = await agent.query("What is the total revenue?")
    
    # Execute SQL queries
    success, data = execute_sql(result['sql'])
    
    # Run evaluation
    async def evaluate():
        dataset = load_dataset()
        results = await text2sql_experiment.arun(
            dataset,
            name="my_evaluation",
            model="gpt-5-mini",
            prompt_file=None,
        )
        return results
"""

from .data_utils import create_sample_dataset, download_booksql_dataset
from .db_utils import SQLiteDB, execute_sql
from .text2sql_agent import Text2SQLAgent
from .evals import load_dataset, text2sql_experiment, execution_accuracy

__all__ = [
    "Text2SQLAgent",
    "execute_sql",
    "SQLiteDB",
    "download_booksql_dataset",
    "create_sample_dataset",
    "load_dataset", 
    "text2sql_experiment",
    "execution_accuracy",
]