Text-to-SQL Engine ========================== The Text-to-SQL agent is a core module which translates the Natural Language question received through the ``/question`` endpoint to SQL. The implementation can leverage business and data logic stored in the Context Store module. to generate accurate SQL given the DB schema. Currently the following NL-to-SQL implementations are included in the codebase: - ``Langchain SQL Agent`` - A wrapper around the `Langchain SQLAgent `_ - ``Langchain SQL Chain`` - A wrapper around the `Langchain SQLChain `_ - ``LlamaIndex SQL Generator`` - A wrapper around the `LlamaIndex SQL Generator `_ - ``Dataherald SQL Agent`` - Our in-house Natural Language-to-SQL agent which uses uses in-context learning Dataherald SQL Agent --------------------------------- The ``dataherald_sqlagent`` is an agent that outperforms the Langchain SQL Agent by 12%-250% in our benchmarking. It does this by leveraging up to 7 tools to generate valid SQL: 1. **QuerySQLDataBaseTool**: Executes a given SQL query on the database and returns the string representation of the results. 2. **GetCurrentTimeTool**: Provides access to the current date and time, allowing the agent to address temporal questions, such as "how much did the income increase this month?" 3. **TablesSQLDatabaseTool**: Returns a list of table names, accompanied by a relevance score indicating their potential relevance to the given question. 4. **SchemaSQLDatabaseTool**: Grants access to the schema of the tables, aiding the agent in locating relevant columns. 5. **InfoRelevantColumns**: Offers additional information about specific columns, which can include descriptions from data analysts, categories, and sample rows, enriching the context for query generation. 6. **ColumnEntityChecker**: Facilitates entity searches within specific columns, presenting a list of syntactically relevant entities from the selected column. 7. **GetFewShotExamples**: Allows the agent to request relevant Question/SQL pairs dynamically. The agent can ask for more examples based on question complexity, fostering adaptive learning. Abstract SQLGenerator Class --------------------------- Base class that all SQL generation classes inherit from. :class:`SQLGenerator` ^^^^^^^^^^^^^^^^^^^^^ This base class defines the common structure for SQL generation classes. .. method:: create_sql_query_status(db, query, response, top_k) :noindex: Creates a SQL query status using provided parameters. :param db: The SQL database instance. :type db: SQLDatabase :param query: The SQL query. :type query: str :param response: The Response instance. :type response: Response :param top_k: The number of results to return. :type top_k: int :return: The updated Response instance with the SQL query status. :rtype: Response .. method:: generate_response(user_question, database_connection, context=None) :noindex: Generates a response to a user question based on the given user question, database connection, and optional context. :param user_question: The user's natural language question. :type user_question: Question :param database_connection: The database connection information. :type database_connection: DatabaseConnection :param context: (Optional) Additional context information. :type context: List[dict], optional :return: The Response containing the generated response. :rtype: Response