chore: import upstream snapshot with attribution

examples/ragas_examples/text2sql/__init__.py

@@ -0,0 +1,53 @@
+"""
+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",
+]