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A dataclass that encapsulates the results of an SQL query generation operation, including the generated query, explanation, confidence score, and metadata about database objects used.

Purpose

This dataclass serves as a structured container for SQL generation results, providing a standardized way to return and pass around information about generated SQL queries. It captures not only the SQL query itself but also contextual information like confidence levels, explanatory text, and tracking of which database tables and columns were referenced. This is particularly useful for SQL generation systems that need to provide transparency and traceability of generated queries.

Source Code

class SqlGenerationResult:
    """Result of SQL generation pass"""
    sql_query: str
    explanation: str
    confidence: float
    tables_used: List[str]
    columns_used: Dict[str, List[str]]

Parameters

Parameter Details

sql_query: The generated SQL query string that can be executed against a database. This is the primary output of the SQL generation process.

explanation: A human-readable explanation of what the SQL query does, useful for documentation, debugging, or user understanding of the generated query.

confidence: A float value (typically between 0.0 and 1.0) indicating the confidence level or quality score of the generated SQL query. Higher values indicate greater confidence in the correctness of the query.

tables_used: A list of table names (strings) that are referenced in the generated SQL query. This helps track which database tables are involved in the query.

columns_used: A dictionary mapping table names (keys) to lists of column names (values) that are used from each table. This provides detailed tracking of which specific columns from which tables are referenced in the query.

Return Value

Instantiation returns a SqlGenerationResult object with all five attributes populated. Since this is a dataclass, the __init__ method is automatically generated and returns an instance of SqlGenerationResult. The class itself doesn't have methods that return values, but instances can be used to access the stored SQL generation metadata.

Class Interface

Methods

Attributes

Dependencies

• dataclasses
• typing

Required Imports

from dataclasses import dataclass
from typing import Dict, List

Usage Example

from dataclasses import dataclass
from typing import Dict, List

@dataclass
class SqlGenerationResult:
    sql_query: str
    explanation: str
    confidence: float
    tables_used: List[str]
    columns_used: Dict[str, List[str]]

# Create an instance with SQL generation results
result = SqlGenerationResult(
    sql_query="SELECT u.name, o.total FROM users u JOIN orders o ON u.id = o.user_id WHERE o.total > 100",
    explanation="This query retrieves user names and order totals for orders exceeding $100 by joining users and orders tables.",
    confidence=0.95,
    tables_used=["users", "orders"],
    columns_used={
        "users": ["name", "id"],
        "orders": ["total", "user_id"]
    }
)

# Access the attributes
print(f"Generated SQL: {result.sql_query}")
print(f"Confidence: {result.confidence}")
print(f"Tables used: {', '.join(result.tables_used)}")
for table, columns in result.columns_used.items():
    print(f"Columns from {table}: {', '.join(columns)}")

Best Practices

• This is an immutable data container by design (dataclass). Treat instances as read-only after creation.
• The confidence value should typically be normalized between 0.0 and 1.0 for consistency across different SQL generation implementations.
• Ensure tables_used list contains unique table names to avoid duplication.
• The columns_used dictionary should only include tables that are actually present in tables_used for consistency.
• When creating instances, validate that the sql_query is a non-empty string and syntactically valid SQL if possible.
• The explanation field should be clear and concise, suitable for end-user consumption or logging purposes.
• Consider using this class as a return type for SQL generation functions to provide comprehensive results in a structured format.
• Since this is a dataclass, it automatically provides __repr__, __eq__, and other useful methods for debugging and comparison.

Tags

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