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A dataclass that encapsulates the results of a table selection operation, including selected tables, reasoning, confidence score, and suggested joins.

Purpose

This dataclass serves as a structured container for the output of a table selection pass in a database query generation system. It stores which tables were selected for a query, the reasoning behind the selection, a confidence score indicating the certainty of the selection, and any suggested join operations between tables. This is typically used in natural language to SQL conversion systems where an LLM needs to determine which database tables are relevant for answering a user's question.

Source Code

class TableSelectionResult:
    """Result of table selection pass"""
    selected_tables: List[str]
    reasoning: str
    confidence: float
    suggested_joins: List[str]

Parameters

Parameter Details

selected_tables: A list of strings representing the names of database tables that were selected as relevant for the query. Each string should be a valid table name from the database schema.

reasoning: A string containing the explanation or rationale for why these particular tables were selected. This provides transparency and helps with debugging or understanding the selection logic.

confidence: A float value (typically between 0.0 and 1.0) representing the confidence level in the table selection. Higher values indicate greater certainty that the selected tables are correct for the query.

suggested_joins: A list of strings describing potential join operations between the selected tables. Each string typically describes a relationship or join condition between two or more tables.

Return Value

Instantiating this class returns a TableSelectionResult object with the four specified attributes. Since this is a dataclass, it automatically generates an __init__ method that accepts all four parameters and returns an instance of TableSelectionResult. The object is immutable by default unless frozen=False is specified in the dataclass decorator.

Class Interface

Methods

Attributes

Dependencies

• dataclasses
• typing

Required Imports

from dataclasses import dataclass
from typing import List

Usage Example

from dataclasses import dataclass
from typing import List

@dataclass
class TableSelectionResult:
    selected_tables: List[str]
    reasoning: str
    confidence: float
    suggested_joins: List[str]

# Create an instance
result = TableSelectionResult(
    selected_tables=['users', 'orders', 'products'],
    reasoning='Selected users table for customer info, orders for transaction data, and products for item details',
    confidence=0.92,
    suggested_joins=[
        'users.id = orders.user_id',
        'orders.product_id = products.id'
    ]
)

# Access attributes
print(result.selected_tables)  # ['users', 'orders', 'products']
print(result.confidence)  # 0.92
print(result.reasoning)  # 'Selected users table for...'
print(result.suggested_joins)  # ['users.id = orders.user_id', ...]

Best Practices

• This is a dataclass, so it automatically generates __init__, __repr__, __eq__, and other methods. No need to define them manually.
• The confidence value should typically be normalized between 0.0 and 1.0 for consistency.
• The selected_tables list should contain valid table names that exist in the database schema being queried.
• The suggested_joins should be formatted consistently (e.g., 'table1.column = table2.column') for easier parsing.
• Since this is a dataclass without frozen=True, instances are mutable. Consider adding frozen=True to the decorator if immutability is desired.
• This class is typically instantiated as the return value from a table selection function or method in a larger query generation pipeline.
• The reasoning field is valuable for debugging and explaining AI decisions to end users.
• When using this in a pipeline, check the confidence score to determine if additional validation or user confirmation is needed for low-confidence selections.

Tags

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