# from deepsearcher.configuration import vector_db, embedding_model, llm
from deepsearcher import configuration
from deepsearcher.vector_db.base import RetrievalResult


def query(original_query: str, **kwargs) -> tuple[str, list[RetrievalResult]]:
    """
    Query the knowledge base with a question and get an answer.

    This function uses the default searcher to query the knowledge base and generate
    an answer based on the retrieved information.

    Args:
        original_query: The question or query to search for.
        max_iter: Maximum number of iterations for the search process.

    Returns:
        A tuple containing:
            - The generated answer as a string
            - A list of retrieval results that were used to generate the answer
    """
    default_searcher = configuration.default_searcher
    max_iter = kwargs.get("max_iter", 3)
    web_search = kwargs.get("web_search", False)
    return default_searcher.query(original_query, max_iter=max_iter, web_search=web_search)