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A dataclass representing a single iteration in an iterative SQL query generation and evaluation workflow, capturing the query, its results, assessment, and improvement suggestions.

Purpose

QueryIteration serves as a data container for tracking each step in an iterative SQL query refinement process. It stores the generated SQL query, explanation, sample data results, quality assessment, improvement suggestions, and satisfaction status. This class is typically used in workflows that iteratively improve SQL queries based on evaluation feedback, allowing the system to track progress and determine when a satisfactory query has been generated.

Source Code

class QueryIteration:
    """Single iteration of SQL query generation and evaluation"""
    iteration_number: int
    sql_query: str
    query_explanation: str
    data_sample: pd.DataFrame
    data_assessment: Dict[str, Any]
    suggested_improvements: List[str]
    is_satisfactory: bool
    improvement_reason: str = None

Parameters

Name	Type	Default	Kind
bases	-	-

Parameter Details

iteration_number: Integer representing the sequence number of this iteration in the query refinement process (e.g., 1 for first attempt, 2 for second, etc.)

sql_query: String containing the SQL query generated in this iteration

query_explanation: String providing a human-readable explanation of what the SQL query does and its purpose

data_sample: Pandas DataFrame containing a sample of the data returned by executing the SQL query

data_assessment: Dictionary containing the evaluation results of the query and data quality, with keys describing various assessment metrics and their values

suggested_improvements: List of strings, each describing a specific improvement that could be made to the query in the next iteration

is_satisfactory: Boolean flag indicating whether this iteration's query meets the requirements and no further refinement is needed

improvement_reason: Optional string explaining why improvements are needed if is_satisfactory is False, or None if the query is satisfactory

Return Value

Instantiation returns a QueryIteration object that encapsulates all information about a single query generation and evaluation cycle. This object is immutable by convention (as a dataclass) and serves as a record in the iteration history.

Class Interface

Methods

Attributes

Name	Type	Description	Scope
iteration_number	int	Sequence number of this iteration in the refinement process	instance
sql_query	str	The SQL query string generated in this iteration	instance
query_explanation	str	Human-readable explanation of what the query does	instance
data_sample	pd.DataFrame	Sample of data returned by executing the SQL query	instance
data_assessment	Dict[str, Any]	Dictionary containing evaluation metrics and quality assessment results	instance
suggested_improvements	List[str]	List of specific improvements suggested for the next iteration	instance
is_satisfactory	bool	Flag indicating whether this query meets requirements and needs no further refinement	instance
improvement_reason	str	Optional explanation of why improvements are needed (None if satisfactory)	instance

Dependencies

• pandas
• typing

Required Imports

from dataclasses import dataclass
import pandas as pd
from typing import Dict, List, Any

Usage Example

import pandas as pd
from dataclasses import dataclass
from typing import Dict, List, Any

# Create a QueryIteration instance
iteration = QueryIteration(
    iteration_number=1,
    sql_query="SELECT * FROM users WHERE age > 18",
    query_explanation="Retrieves all users older than 18 years",
    data_sample=pd.DataFrame({'id': [1, 2], 'name': ['Alice', 'Bob'], 'age': [25, 30]}),
    data_assessment={'row_count': 2, 'completeness': 1.0, 'quality_score': 0.95},
    suggested_improvements=['Add ORDER BY clause', 'Consider adding LIMIT'],
    is_satisfactory=False,
    improvement_reason="Query lacks ordering and pagination"
)

# Access attributes
print(f"Iteration {iteration.iteration_number}")
print(f"Query: {iteration.sql_query}")
print(f"Satisfactory: {iteration.is_satisfactory}")
print(f"Sample data shape: {iteration.data_sample.shape}")

# Check if improvements are needed
if not iteration.is_satisfactory:
    print(f"Improvements needed: {', '.join(iteration.suggested_improvements)}")
    print(f"Reason: {iteration.improvement_reason}")

Best Practices

• This is a dataclass, so it automatically generates __init__, __repr__, and __eq__ methods
• Treat instances as immutable records - do not modify attributes after creation
• Use this class to build a history of iterations by creating a new instance for each refinement cycle
• The improvement_reason field should be populated when is_satisfactory is False to provide context
• Ensure data_sample is a valid pandas DataFrame before instantiation
• The data_assessment dictionary should follow a consistent schema across iterations for easier comparison
• Consider serializing instances to JSON for logging or persistence (may require custom serialization for DataFrame)
• This class is typically instantiated by workflow orchestration code, not directly by end users
• Use iteration_number to track progress and detect infinite loops in refinement workflows

Tags

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class QueryIteration:
    """Single iteration of SQL query generation and evaluation"""
    iteration_number: int
    sql_query: str
    query_explanation: str
    data_sample: pd.DataFrame
    data_assessment: Dict[str, Any]
    suggested_improvements: List[str]
    is_satisfactory: bool
    improvement_reason: str = None