types.DataSource
An abstract class defining the interface for data sources used by QueryChat.
This class is generic over the DataFrame type returned by execute_query, test_query, and get_data methods.
Attributes
| table_name |
str |
Name of the table to be used in SQL queries. |
Methods
cleanup
types.DataSource.cleanup()
Clean up resources associated with the data source.
This method should clean up any connections or resources used by the data source.
execute_query
types.DataSource.execute_query(query)
Execute SQL query and return results.
Parameters
| query |
str |
SQL query to execute |
required |
get_data
types.DataSource.get_data()
Return the unfiltered data.
Returns
|
IntoFrameT |
The complete dataset |
get_db_type
types.DataSource.get_db_type()
Name for the database behind the SQL execution.
get_schema
types.DataSource.get_schema(categorical_threshold)
Return schema information about the table as a string.
Parameters
| categorical_threshold |
int |
Maximum number of unique values for a text column to be considered categorical |
required |
Returns
|
str |
A string containing the schema information in a format suitable for prompting an LLM about the data structure |
get_semantic_views_description
types.DataSource.get_semantic_views_description()
Get information about semantic views (if any) for the system prompt.
test_query
types.DataSource.test_query(query, *, require_all_columns=False)
Test SQL query by fetching only one row.
Parameters
| query |
str |
SQL query to test |
required |
| require_all_columns |
bool |
If True, validates that result includes all original table columns. Additional computed columns are allowed. |
False |
Returns
|
IntoFrameT |
Query results with at most one row |
Raises
|
MissingColumnsError |
If require_all_columns is True and result is missing required columns |